Information processing apparatus, information processing method, and program

Abstract

To provide an information processing apparatus that can suitably improve explainability in an optimization method referring to a plurality of models.SOLUTION: An information processing apparatus comprises: an acquisition unit that acquires target data, the number of models, and information on the correspondence between the target data and target models; a regression coefficient calculation unit that refers to the target data and a ratio parameter defining the ratio between the target models to calculate regression coefficients; a ratio parameter calculation unit that refers to the target data and the regression coefficients to calculate the ratio parameter; and a storage unit that stores the regression coefficients and the ratio parameter in association with the correspondence.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] There are known techniques for learning machine learning models (also simply called models). As an example, Patent Document 1 discloses a machine learning model used for controlling the operation of a robot. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2021 / 039995 Summary of the Invention [Problem to be solved by the invention]

[0004] On the other hand, optimization or control methods that refer to the results of multiple models are also known. One example is the Linear Parameter-Varying (LPV) model, which is expressed as a weighted sum of multiple models. However, methods that refer to multiple models have had issues with the explainability of learning or prediction results.

[0005] The present disclosure has been made in consideration of the above-mentioned problems, and an exemplary purpose thereof is to provide a technology that can suitably improve explainability in an optimization method that references multiple models. [Means for solving the problem]

[0006] An information processing device according to an exemplary aspect of the present disclosure includes an acquisition means for acquiring information regarding target data, the number of models of a plurality of target models, and the correspondence between the target data and the plurality of target models; a regression coefficient calculation means for calculating a regression coefficient for each of the plurality of target models by referring to a ratio parameter that defines a ratio of the plurality of target models and the target data; a ratio parameter calculation means for calculating the ratio parameter by referring to the target data and the target data; and a storage means for storing the regression coefficient and the ratio parameter calculated by the ratio parameter calculation means in association with the correspondence.

[0007] An information processing device according to an exemplary aspect of the present disclosure includes an acquisition means for acquiring inference data and information relating to the type of the inference data, a prediction means for deriving a prediction result by applying to the inference data a regression coefficient for each of a plurality of target models and a ratio parameter that specifies a ratio of the plurality of target models, the ratio parameter being determined according to the type of the inference data, and an output means for outputting the prediction result by the prediction means, wherein the regression coefficients and the ratio parameters are regression coefficients and ratio parameters learned by a learning process including a regression coefficient calculation process that calculates a regression coefficient for each of a plurality of target models by referring to the ratio parameter and learning data, and a ratio parameter calculation process that calculates the ratio parameter by referring to the learning data and the regression coefficients.

[0008] An information processing method according to an exemplary aspect of the present disclosure includes an acquisition process in which at least one processor acquires information regarding target data, the number of models of a plurality of target models, and the correspondence between the target data and the plurality of target models; a regression coefficient calculation process in which a regression coefficient for each of the plurality of target models is calculated by referring to a ratio parameter that defines a ratio of the plurality of target models and the target data; a ratio parameter calculation process in which the ratio parameter is calculated by referring to the target data and the regression coefficient; and a storage process in which the regression coefficient and the ratio parameter calculated in the ratio parameter calculation process are stored in association with the correspondence.

[0009] The information processing device according to each aspect of the present invention may be realized by a computer, in which case a program that causes the computer to operate as each part (software element) of the information processing device to realize the information processing device also falls within the scope of the present invention. [Effects of the Invention]

[0010] According to one exemplary aspect of the present disclosure, an exemplary effect is achieved in that a technology that can suitably improve explainability in an optimization method that references multiple models can be provided. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 2] FIG. 1 is a flow diagram showing the flow of an information processing method according to the present disclosure. [Figure 3] 1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 4] FIG. 1 is a flow diagram showing the flow of an information processing method according to the present disclosure. [Figure 5] 1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 6] FIG. 10 is a diagram schematically illustrating the output of each endpoint model in the LPV model according to the present disclosure and the interior division ratio parameters by which each output is multiplied. [Figure 7] FIG. 10 is a diagram illustrating an example of a processing flow in an information processing device according to the present disclosure. [Figure 8] 10 shows an example of a graph displayed by an output unit via an input / output unit according to the present disclosure. [Figure 9] 10 shows another example of a graph displayed by the output unit via the input / output unit according to the present disclosure. [Figure 10] 10 shows yet another example of a graph displayed by the output unit via the input / output unit according to the present disclosure. [Figure 11]1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 12] 1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 13] FIG. 10 is a diagram illustrating an example of a processing flow in an information processing device according to the present disclosure. [Figure 14] FIG. 1 is a block diagram illustrating a configuration of a computer that functions as an information processing device according to the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0012] The following are examples of embodiments of the present invention. However, the present invention is not limited to the exemplary embodiments shown below, and various modifications are possible within the scope of the claims. For example, embodiments obtained by appropriately combining the technical means employed in the exemplary embodiments shown below may also be included in the scope of the present invention. Furthermore, embodiments obtained by appropriately omitting some of the technical means employed in the exemplary embodiments shown below may also be included in the scope of the present invention. Furthermore, the effects mentioned in the exemplary embodiments shown below are examples of effects expected in the exemplary embodiments, and do not define the scope of the present invention. In other words, embodiments that do not exhibit the effects mentioned in the exemplary embodiments shown below may also be included in the scope of the present invention.

[0013] First Exemplary Embodiment A first exemplary embodiment, which is one example of an embodiment of the present invention, will be described in detail with reference to the drawings. This exemplary embodiment is the basic form of each exemplary embodiment described later. Note that the scope of application of each technical means employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technical means employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure to the extent that no particular technical obstacles arise. Furthermore, each technical means shown in the drawings referred to in describing this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure to the extent that no particular technical obstacles arise.

[0014] (Configuration of information processing device 1) The configuration of the information processing device 1 will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the configuration of the information processing device 1. As shown in Fig. 1, the information processing device 1 includes an acquisition unit 11, a regression coefficient calculation unit 12, a ratio parameter calculation unit 14, and an output unit 15. In this exemplary embodiment, the acquisition unit 11, the regression coefficient calculation unit 12, the ratio parameter calculation unit 14, and the output unit 15 respectively realize an acquisition means, a regression coefficient calculation means, a ratio parameter calculation means, and a storage means.

[0015] (Acquisition part 11) The acquisition unit 11 acquires target data, the number of models of the multiple target models, and information regarding the correspondence between the target data and the multiple target models. The acquisition unit 11 supplies the acquired target data to the regression coefficient calculation unit 12 and the ratio parameter calculation unit 14. The acquisition unit 11 also supplies the acquired number of models of the multiple target models to the regression coefficient calculation unit 12. The acquisition unit 11 also supplies information regarding the correspondence between the acquired target data and the multiple target models to the output unit 15.

[0016] (Regression coefficient calculation part 12) The regression coefficient calculation unit 12 calculates a regression coefficient for each of the plurality of target models by referring to the target data and a ratio parameter that defines the ratio of the plurality of target models. The regression coefficient calculation unit 12 supplies the calculated regression coefficient to the ratio parameter calculation unit 14 and the output unit 15.

[0017] (Ratio parameter calculation part 14) The ratio parameter calculation unit 14 calculates the ratio parameters by referring to the target data and the regression coefficients. The ratio parameter calculation unit 14 supplies the calculated ratio parameters to the output unit 15.

[0018] (Output section 15) The output unit 15 stores the regression coefficients and the ratio parameters calculated by the ratio parameter calculation unit 14 in association with a correspondence relationship. As an example, the output unit 15 supplies the regression coefficients and the ratio parameters in association with a correspondence relationship to a storage unit (not shown).

[0019] (Effects of information processing device 1) As described above, the information processing device 1 employs a configuration including an acquisition unit 11 that acquires information regarding the target data, the number of models of a plurality of target models, and the correspondence between the target data and the plurality of target models, a regression coefficient calculation unit 12 that calculates a regression coefficient for each of the plurality of target models by referring to the target data and a ratio parameter that defines the ratio of the plurality of target models, a ratio parameter calculation unit 14 that calculates the ratio parameter by referring to the target data and the regression coefficient, and an output unit 15 that stores the regression coefficients and the ratio parameters calculated by the ratio parameter calculation unit 14 in association with the correspondence. Therefore, the information processing device 1 has the effect of suitably improving explainability in an optimization method that refers to a plurality of models by using information regarding the correspondence.

[0020] (Flow of information processing method S1) The flow of the information processing method S1 will be described with reference to Fig. 2. Fig. 2 is a flow diagram showing the flow of the information processing method S1. As shown in Fig. 2, the information processing method S1 includes an acquisition process S11, a regression coefficient calculation process S12, a ratio parameter calculation process S14, and a storage process S15.

[0021] (Acquisition process S11) In acquisition processing S11, the acquisition unit 11 acquires target data, the number of models of the multiple target models, and information regarding the correspondence between the target data and the multiple target models. The acquisition unit 11 supplies the acquired target data to the regression coefficient calculation unit 12 and the ratio parameter calculation unit 14. The acquisition unit 11 also supplies the acquired number of models of the multiple target models to the regression coefficient calculation unit 12. The acquisition unit 11 also supplies information regarding the correspondence between the acquired target data and the multiple target models to the output unit 15.

[0022] (Regression coefficient calculation process S12) In the regression coefficient calculation process S12, the regression coefficient calculation unit 12 calculates a regression coefficient for each of the plurality of target models by referring to the target data and a ratio parameter that defines the ratio of the plurality of target models. The regression coefficient calculation unit 12 supplies the calculated regression coefficient to the ratio parameter calculation unit 14 and the output unit 15.

[0023] (Ratio parameter calculation process S14) In the ratio parameter calculation process S14, the ratio parameter calculation unit 14 calculates the ratio parameters by referring to the target data and the regression coefficients. The ratio parameter calculation unit 14 supplies the calculated ratio parameters to the output unit 15.

[0024] (Output process S15) In the output process S15, the output unit 15 stores the regression coefficients and the ratio parameters calculated by the ratio parameter calculation unit 14 in association with the correspondence relationship.

[0025] (Effect of information processing method S1) As described above, the information processing method S1 employs a configuration including an acquisition process S11 in which the acquisition unit 11 acquires information regarding the target data, the number of models of a plurality of target models, and the correspondence between the target data and the plurality of target models, a regression coefficient calculation process S12 in which the regression coefficient calculation unit 12 calculates a regression coefficient for each of the plurality of target models by referring to the target data and a ratio parameter that defines the ratio of the plurality of target models, a ratio parameter calculation process S14 in which the ratio parameter calculation unit 14 calculates a ratio parameter by referring to the target data and the regression coefficient, and an output process S15 in which the output unit 15 stores the regression coefficients and the ratio parameters calculated by the ratio parameter calculation unit 14 in association with the correspondence. Therefore, the information processing method S1 can achieve the same effects as the information processing device 1 described above.

[0026] (Configuration of information processing device 2) The configuration of the information processing device 2 will be described with reference to Fig. 3. Fig. 3 is a block diagram showing the configuration of the information processing device 2. As shown in Fig. 3, the information processing device 2 includes an acquisition unit 21, a prediction unit 22, and an output unit 23. In this exemplary embodiment, the acquisition unit 21, the prediction unit 22, and the output unit 23 respectively realize an acquisition means, a prediction means, and an output means.

[0027] (Acquisition part 21) The acquisition unit 21 acquires inference data and information relating to the type of the inference data. The acquisition unit 21 supplies the acquired inference data and information relating to the type of the inference data to the prediction unit 22.

[0028] (Prediction Section 22) The prediction unit 22 derives a prediction result by applying, to the inference data, regression coefficients for each of the plurality of target models and ratio parameters that define ratios for the plurality of target models and are determined according to the type of inference data. The prediction unit 22 supplies the derived prediction result to the output unit 23.

[0029] (output unit 23) The output unit 23 outputs the prediction result from the prediction unit 22.

[0030] The regression coefficients and ratio parameters are regression coefficients and ratio parameters learned by a learning process including a regression coefficient calculation process that calculates a regression coefficient for each of a plurality of target models by referring to the ratio parameters and learning data, and a ratio parameter calculation process that calculates the ratio parameters by referring to the learning data and the regression coefficients. As an example, the regression coefficients and ratio parameters are regression coefficients and ratio parameters, respectively, calculated by the information processing device 1 according to this exemplary embodiment.

[0031] (Effects of information processing device 2) As described above, the information processing device 2 employs a configuration including an acquisition unit 21 that acquires inference data and information related to the type of the inference data, a prediction unit 22 that derives a prediction result by applying, to the inference data, regression coefficients for each of a plurality of target models and ratio parameters that define ratios for the plurality of target models, the ratio parameters being determined according to the type of inference data, and an output unit 23 that outputs the prediction result obtained by the prediction unit 22. The regression coefficients and ratio parameters are regression coefficients and ratio parameters learned through a learning process that includes a regression coefficient calculation process that calculates a regression coefficient for each of the plurality of target models by referencing the ratio parameters and training data, and a ratio parameter calculation process that calculates the ratio parameters by referencing the training data and the regression coefficients. Thus, the information processing device 2 derives a prediction result using ratio parameters determined according to the type of inference data, thereby advantageously improving explainability.

[0032] (Flow of information processing method S2) The flow of the information processing method S2 will be described with reference to Fig. 4. Fig. 4 is a flow diagram showing the flow of the information processing method S2. As shown in Fig. 4, the information processing method S2 includes an acquisition process S21, a prediction process S22, and an output process S23.

[0033] (Acquisition process S21) In the acquisition process S21, the acquisition unit 21 acquires inference data and information about the type of the inference data. The acquisition unit 21 supplies the acquired inference data and information about the type of the inference data to the prediction unit 22.

[0034] (Prediction process S22) In the prediction process S22, the prediction unit 22 derives a prediction result by applying, to the inference data, regression coefficients for each of the plurality of target models and ratio parameters that define ratios for the plurality of target models and are determined according to the type of inference data. The prediction unit 22 supplies the derived prediction result to the output unit 23.

[0035] (Output process S23) In the output process S23, the output unit 23 outputs the prediction result by the prediction unit 22.

[0036] The regression coefficients and ratio parameters are regression coefficients and ratio parameters learned by a learning process including a regression coefficient calculation process that calculates a regression coefficient for each of a plurality of target models by referring to the ratio parameters and learning data, and a ratio parameter calculation process that calculates the ratio parameters by referring to the learning data and the regression coefficients. As an example, the regression coefficients and ratio parameters are the regression coefficients and ratio parameters calculated by the information processing method S1 according to this exemplary embodiment.

[0037] (Effect of information processing method S2) As described above, the information processing method S2 includes an acquisition process S21 in which the acquisition unit 21 acquires inference data and information related to the type of the inference data; a prediction process S22 in which the prediction unit 22 derives a prediction result by applying, to the inference data, regression coefficients for each of a plurality of target models and ratio parameters that define ratios for the plurality of target models and are determined according to the type of inference data; and an output process S23 in which the output unit 23 outputs the prediction result obtained by the prediction unit 22. The regression coefficients and ratio parameters are regression coefficients and ratio parameters learned by a learning process that includes a regression coefficient calculation process that calculates a regression coefficient for each of the plurality of target models by referencing the ratio parameters and training data, and a ratio parameter calculation process that calculates the ratio parameters by referencing the training data and the regression coefficients. Therefore, the information processing method S2 achieves the same effects as the information processing device 1 described above.

[0038] Second Exemplary Embodiment A second exemplary embodiment, which is one example of an embodiment of the present invention, will be described in detail with reference to the drawings. Components having the same functions as those described in the above exemplary embodiment will be assigned the same reference numerals, and their description will be omitted as appropriate. The scope of application of each technical means employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technical means employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise. Furthermore, each technical means shown in each drawing referenced to explain this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise.

[0039] The positioning of the algorithm of the processing by the information processing device 1A according to this exemplary embodiment will be described. The present inventors have been studying a Linear Parameter-Varying (LPV) model as a modeling of a system with fluctuations. In the LPV model, as an example, an internal state quantity (internal state variable) x k , and the output state quantity (output state variable) y k is updated and calculated using the following formulas (1A) and (1B).

[0040]

number

[0041] where A (i) , B (i) is a matrix representing each state-space model (also called each end point model) that is distinguished from each other by index i, and μ (i) k is a parameter that defines the internal division ratio (weight) of each model. μ (i) k These are called internal ratio parameters, weight parameters, or scheduling parameters. kare input variables, and C and D are x k and u k k is an index assigned to each state variable, and is, for example, time.

[0042] Figure 6 shows the output of each end model in the LPV model (1st SS model to 5th SS model in Figure 6) and the internal division ratio parameter μ (i) k As shown in Fig. 6, the output of multiple endpoint models at the k-th step is (A (i) x k +B (i) u k ) (i=1~5) For each of the internal division ratio parameters μ (i) k (i=1~5) is multiplied, and x at the k+1th step is k+1 is calculated.

[0043] While such an LPV model is suitable for modeling systems with fluctuations, the internal ratio parameter μ (i) k However, there was a problem that it was difficult to apply to systems where the value of is unknown.

[0044] The present inventors The above internal division ratio parameter μ (i) k The hidden variable (posterior probability) z k Treat it as - Applying hidden variable model learning methods in machine learning, The above internal division ratio parameter μ (i) k the hidden variable z k Calculate as the expected value of By doing so, the inventors have found that it is possible to realize the learning of an LPV model even if the interior ratio parameter μ (i)k The L2PV model (Latent Linear Parameter-Varying model) is defined by the following equations (2A) to (2C), which introduce the following as hidden variables:

[0045]

number

[0046] The following equations (3A) to (3E) are used:

[0047]

number

[0048] By rewriting it into the regression model form (L2PV regression model) specified by (i) k This gave rise to the idea of ​​using this as a learning subject.

[0049] Each process performed by the information processing device 1A described below is based on the above-mentioned formulation and is a process based on the unique viewpoint of the inventor.

[0050] (Configuration of information processing device 1A) The configuration of the information processing device 1A will be described with reference to Fig. 5. Fig. 5 is a block diagram showing the configuration of the information processing device 1A. As shown in Fig. 5, the information processing device 1A includes a control unit 10A, a storage unit 15A, a communication unit 16A, and an input / output unit 17A.

[0051] (Storage section 15A) First, various data (information) stored in the storage unit 15A will be described. Data referenced by the control unit 10A is stored in the storage unit 15A. Examples of the storage unit 15A include, but are not limited to, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), or a combination thereof.

[0052] Examples of data stored in the memory unit 15A include, but are not limited to, target data TD, ratio parameter RP, regression coefficient RC, correspondence relationship information CI, distribution information DI, learning result LR, inference data PD, and prediction result PR, as shown in FIG. 5.

[0053] The target data TD is data used in the learning process in the information processing device 1A. The target data TD is a state variable (~x k ) and state variables (~y k ) is expressed as the following equation (4). k ,~x k ,y k ,~y k are sometimes called features. Also, the state variable x k ,~x k are called explanatory variables, and the state variable y k ,~y k The objective variable is sometimes called a target variable. Furthermore, when the objective variable is the target of derivation, the objective variable is sometimes called a predicted value. These specific names do not limit the contents described in this specification.

[0054]

number

[0055] The ratio parameter RP is a parameter that defines the relative weights of multiple state space models in an LPV model, and is also referred to as a scheduling parameter. The ratio parameter RP may be an internal ratio parameter or an external ratio parameter. In this exemplary embodiment, in order to describe the case where the ratio parameter RP is an internal ratio parameter, the ratio parameter RP is also referred to as an internal ratio parameter RP. The ratio parameter RP is also referred to as a weight parameter RP or a scheduling parameter RP. As an example, the ratio parameter RP is given by the following equation (5) corresponding to each of m models (model 1 to model m):

[0056]

number

[0057] Here, k is an index similar to the index assigned to each state variable described above, and N represents the dimension of each state variable (the number of samples of each state variable). Also, the index (i) relating to the model is not explicitly stated in the above expression. This is because the ratio parameter RP is divided into an internal ratio parameter vector consisting of components corresponding to models 1 to m for each k. μ k =(μ k (1) , μ k (2) , , μ k (m) ) In this way, the ratio parameter RP may be expressed as an internal ratio parameter vector or a ratio parameter matrix.

[0058] Also, the internal ratio parameter μ for a certain model j k (j) is the N-dimensional target data x k It can also be expressed as the components of an N-dimensional vector having components corresponding to each of the j-th internal division ratio parameters μ k (j) is the N-dimensional target data x k (k=1 to N) (j) , μ2 (j) , , μ N (j) ) are the components of an N-dimensional vector with

[0059] The regression coefficient RC is a coefficient in the L2PV regression model. The regression coefficient RC is expressed as the following equation (6).

[0060]

number

[0061] The correspondence information CI is information about the correspondence between the target data TD and a plurality of target models. Examples of the information about the correspondence include: -Information indicating what kind of target data TD each of the multiple target models has been trained (will be trained) with is included. For example, the correspondence relationship information CI relating to the target models a, b, and c and the target data TD1, TD2, and TD3 is as follows: Target model a: Target data TD1, TD2 Target model b: Target data TD3 Target model c: Target data TD1, TD2, TD3 Here, the first line in the above example indicates that the target model a has been trained (will be trained) using the target data TD1 and TD2. The same applies to the other lines.

[0062] In other words, the correspondence information CI is Information indicating whether each of the multiple target models has used (will use) each of the multiple target data TD as training data As an example, the correspondence relationship information CI relating to the target models a, b, and c and the target data TD1, TD2, and TD3 may be expressed as follows: Target model a: Target data TD1 (○), TD2 (○), TD3 (×) Target model b: Target data TD1(×), TD2(×), TD3(○) Target model c: Target data TD1(○), TD2(○), TD3(○) Here, ○ indicates that it was used (will be used) for learning, and × indicates that it was not used (will not be used) for learning.

[0063] In the above example, the correspondence relationship information CI may include information about the circumstances under which each piece of target data was acquired. Target data TD1 (e.g., sales volume): Situation A (e.g., sunny weather) Target data TD2 (e.g., sales volume): Situation B (e.g., cloudy weather) Target data TD3 (e.g., sales volume): Situation C (e.g., the weather is rainy) In addition, when the correspondence information CI includes the correspondence between each target data and the situation in which the target data was acquired in this way, the correspondence information CI may be expressed as including the relationship between each target model and information related to the target model. For example, in the above example, The correspondence information CI is Target model a: Situation A (e.g., sunny weather), Situation B (e.g., cloudy weather) Target model b: Situation C (e.g., rainy weather) Target model c: Situations A, B, and C The correspondence relationship information CI may include information indicating a correspondence such as the following. By including the above information in the correspondence relationship information CI, the information processing device 1A can identify what situation each target model is associated with, in other words, what situation each target model is preferably used in. Furthermore, the information processing device 1A can also present to the user what situation each target model is preferably used in.

[0064] The information about situations such as situations A, B, and C may be expressed as information about types. In other words, the correspondence relationship information CI may include information indicating which type at least one of the target model and the target data is associated with.

[0065] In addition, the correspondence information CI is Information indicating whether a certain target data TD is similar to other target data TD Here, the target data TD and other target data TD being similar refers to the case where the types of the target data and other target data TD are similar. For example, if the target data TD is data on the number of sales acquired in situation A (e.g., sunny weather), then the target data TD', which is data on the number of sales in situation A' (e.g., sunny weather) similar to situation A, is data similar to the target data TD.

[0066] The distribution information DI includes a covariance matrix Φ of the prior distribution of the latent variables, a covariance parameter η of the prior distribution of the latent variables, and a covariance parameter Ψ of the posterior distribution of the latent variables.

[0067] Hidden variable z k The prior distribution p(z k ) is expressed as the following equation (7).

[0068]

number

[0069] Hidden variable z k In other words, the covariance parameter η of the prior distribution of k |z k ,~x k ,W,η) is the covariance parameter η.

[0070]

number

[0071] Hidden variable z k The posterior distribution p(z k |~y k ,~x k ,W,η) is expressed as the following equation (9).

[0072]

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[0073] In the above formula, the calligraphy font N on the right side represents a normal distribution. However, this does not mean that the example of the distribution in this exemplary embodiment is limited to a normal distribution. As an example, the hidden variable z k The Dirichlet distribution may be used as the posterior distribution of .

[0074] As will be described later, in the processing by the information processing device 1A, the hidden variable z k The posterior distribution p(z k |~y k ,~x k , W, η) is expressed under the constraints (constraints) of the following equation (10).

[0075]

number

[0076] Therefore, the hidden variable z k Even when a normal distribution is used as the posterior distribution of , a suitable calculation can be performed.

[0077] The learning result LR is data that is output by the output unit 15, which will be described later. The learning result LR includes the calculated ratio parameter RP and the calculated regression coefficient RC.

[0078] The inference data PD is an internal state quantity input to the L2PV regression model. The L2PV regression model makes predictions by using the inference data PD as input and applying the calculated regression coefficients RC to each of multiple target models.

[0079] The predicted result PR is a predicted result obtained by the L2PV regression model. Examples of the predicted result PR will be described later.

[0080] (Communication unit 16A) The communication unit 16A is an interface for transmitting and receiving data via a network. Examples of the communication unit 16A include, but are not limited to, communication chips for various communication standards such as Ethernet (registered trademark), Wi-Fi (Wireless Fidelity (registered trademark), and wireless communication standards for mobile data communication networks, and USB-compliant connectors.

[0081] (Input / output section 17A) The input / output unit 17A is an interface that receives input of data and outputs data. Examples of the input / output unit 17A include, but are not limited to, a microphone, a camera, a gaze input device, a keyboard, a touchpad, a speaker, and a liquid crystal display.

[0082] (Control unit 10A) The control unit 10A controls each component included in the information processing device 1A. As shown in FIG. 5 , the control unit 10A includes an acquisition unit 11, a regression coefficient calculation unit 12, a covariance calculation unit 13, a ratio parameter calculation unit 14, an output unit 15, an initial value determination unit 16, a convergence determination unit 17, and a prediction unit 18. In this exemplary embodiment, the acquisition unit 11, the regression coefficient calculation unit 12, the covariance calculation unit 13, the ratio parameter calculation unit 14, the initial value determination unit 16, the convergence determination unit 17, and the prediction unit 18 function as an acquisition unit, a regression coefficient calculation unit, a covariance calculation unit, a ratio parameter calculation unit, an initial value determination unit, a convergence determination unit, and a prediction unit, respectively. In this exemplary embodiment, the output unit 15 functions as a storage unit and an output unit. Specific examples of the processing performed by each unit will be described later with reference to different drawings.

[0083] The acquisition unit 11 acquires data via the communication unit 16A or the input / output unit 17A. Examples of the data acquired by the acquisition unit 11 include target data TD, the number of models of a plurality of target models, and correspondence relationship information CI. Another example of the data acquired by the acquisition unit 11 is the latent variable z k , information on the prior distribution of the data PD. Inference data PD is another example of data acquired by the acquisition unit 11. The acquisition unit 11 stores the acquired data in the storage unit 15A.

[0084] The regression coefficient calculation unit 12 calculates a regression coefficient RC for each of the plurality of target models by referring to a ratio parameter RP that defines the ratio of the plurality of target models and the target data TD. As one example, the ratio parameter RP referred to by the regression coefficient calculation unit 12 is the initial value of the ratio parameter RP determined by an initial value determination unit 16, which will be described later. As another example, the ratio parameter RP referred to by the regression coefficient calculation unit 12 is the ratio parameter RP calculated by a ratio parameter calculation unit 14, which will be described later. The regression coefficient calculation unit 12 stores the calculated regression coefficient RC in the storage unit 15A.

[0085] The covariance calculation unit 13 calculates the target data TD, the ratio parameter RP, the regression coefficient RC, and the latent variable z k By referring to the covariance matrix Φ of the prior distribution of the hidden variable z k The covariance parameter η of the prior distribution of and the hidden variable z k The covariance calculation unit 13 calculates the covariance matrix Ψ of the posterior distribution of the calculated hidden variable z k The covariance parameter η of the prior distribution of and the hidden variable z k and the covariance matrix Ψ of the posterior distribution of the above are stored in the storage unit 15A as distribution information DI.

[0086] The ratio parameter calculation unit 14 calculates the ratio parameter RP by referring to the target data TD and the regression coefficient RC. k The ratio parameter calculation unit 14 further refers to the covariance matrix Ψ of the posterior distribution of the ratio parameter RP to calculate the ratio parameter RP. The ratio parameter calculation unit 14 stores the calculated ratio parameter RP in the storage unit 15A.

[0087] Furthermore, the ratio parameter calculation unit 14 refers to the correspondence information CI and calculates the ratio parameter RP under a constraint condition according to the correspondence. As an example, the ratio parameter calculation unit 14 calculates the ratio parameter RP under a constraint condition on the sum of the ratio parameters RP according to the correspondence.

[0088] Furthermore, the ratio parameter calculation unit 14 calculates the ratio parameter RP using an update equation that includes a normalization term according to the correspondence relationship.

[0089] The output unit 15 stores the regression coefficients RC and the ratio parameters RP in association with the correspondence. As an example, the output unit 15 associates the regression coefficients RC and the ratio parameters RP with correspondence information CI and supplies them to the storage unit 15A.

[0090] Furthermore, the output unit 15 outputs the regression coefficient RC and the ratio parameter RP (learning result LR) calculated by the ratio parameter calculation unit 14. As one example, the output unit 15 outputs an image including the regression coefficient RC and the ratio parameter RP calculated by the ratio parameter calculation unit 14 to the input / output unit 17A. In this configuration, the output unit 15 may display graphs defined by the regression coefficients RC for at least two of the multiple target models in a manner that allows them to be distinguished from one another. As another example, the output unit 15 outputs the regression coefficient RC and the ratio parameter RP calculated by the ratio parameter calculation unit 14 when the convergence determination unit 17 described below determines that the calculation related to the ratio parameter RP has converged.

[0091] The initial value determination unit 16 determines the initial value of the ratio parameter RP to be referred to by the regression coefficient calculation unit 12. The initial value determination unit 16 stores the determined initial value of the ratio parameter RP in the storage unit 15A.

[0092] The convergence determination unit 17 determines whether or not the calculation regarding the ratio parameter RP has converged. The convergence determination unit 17 supplies the determination result to the output unit 15.

[0093] The prediction unit 18 derives a prediction result PR by applying, to the inference data PD, the regression coefficients RC for each of the plurality of target models and the ratio parameters RP determined according to the type of the inference data PD, among the ratio parameters RP calculated by the ratio parameter calculation unit 14. The prediction unit 18 stores the derived prediction result PR in the storage unit 15A.

[0094] (Example of processing flow in information processing device 1A) 7 is a diagram showing an example of a processing flow in the information processing device 1A according to this exemplary embodiment. Note that the processing example described below can also be regarded as a variational Bayes EM algorithm, but this does not limit this exemplary embodiment. Furthermore, the processing example described below can be regarded as processing for updating each parameter so as to maximize a variational lower bound (VLB) J obtained by the following equation (11).

[0095]

number

[0096] The processing example described below can also be expressed as an algorithm for solving a maximum likelihood problem defined by the model likelihood p in the following equation (12).

[0097]

number

[0098] (Step S11: Acquisition process) In step S11, the acquisition unit 11 acquires target data TD. Here, as described above, the target data TD is data used for the learning process in the information processing device 1A. Details of the target data TD have been explained, so explanation will be omitted here.

[0099] In step S11, the acquisition unit 11 further acquires a parameter m indicating the number of models among the plurality of target models. Here, the number of models m is determined by the internal division ratio parameter vector μ k (i) It can also be expressed as the number of

[0100] In addition, in step S11, the acquisition unit 11 acquires correspondence relationship information CI relating to correspondence relationships between the target data TD and a plurality of target models.

[0101] In step S11, the acquisition unit 11 calculates the hidden variable z k As an example, the acquisition unit 11 acquires information about the prior distribution of the hidden variable z k The prior distribution p(z k ) covariance matrix Φ. The acquisition unit 11 also acquires the hidden variable z k The prior distribution p(z k ) may further be obtained.

[0102] (Step S16: Initial value determination process) Next, in step S16, the initial value determination unit 16 determines the initial value of a ratio parameter RP to be referenced in the regression coefficient calculation process S12, which will be described later. As an example, the initial value determination unit 16 determines the initial value of the ratio parameter RP as a random value. By the initial value determination unit 16 determining the initial value of the ratio parameter RP in this way, the regression coefficient RC can be suitably calculated in the regression coefficient calculation process S12, which will be described later. The details of the ratio parameter RP have been explained above, so a detailed explanation will be omitted here.

[0103] (Step S12: Regression coefficient calculation process) Subsequently, in step S12, the regression coefficient calculation unit 12 calculates the ratio parameter (internal division ratio parameter vector) RP expressed as the following equation (13):

[0104]

number

[0105] and the target data TD expressed as the following equation (14):

[0106]

number

[0107] and a regression coefficient RC for each of the plurality of target models, which is expressed as the following equation (15) with reference to

[0108]

number

[0109] As an example, the regression coefficient calculation unit 12 calculates a regression coefficient RC expressed as the following equation (17) by referring to the ratio parameter RP and the target data TD using the following equation (16).

[0110]

number

[0111]

number

[0112] Here, the asterisk on the shoulder of W indicates the updated value, and in the calculation formula, the operation symbol with a cross in a circle represents the Kronecker product. T represents transposition. Also, Ψ k is the hidden variable z k represents the covariance parameter of the posterior distribution of

[0113] (Step S13: Covariance calculation process) Subsequently, in step S13, the covariance calculation unit 13 calculates the target data TD expressed as the following equation (18):

[0114]

number

[0115] and the ratio parameter (internal division ratio parameter vector) RP expressed as the following equation (19):

[0116]

number

[0117] and the regression coefficient RC expressed as the following equation (20):

[0118]

number

[0119] and the hidden variable z k The hidden variables z k The covariance parameter η of the prior distribution of the hidden variable z k The covariance matrix of the posterior distribution of {Ψ k} k=1 N As an example, the covariance calculation unit 13 calculates the following equation (21):

[0120]

number

[0121] (where Λ k is given by the following equation (22)

[0122]

number

[0123] By the hidden variable z k The covariance matrix of the posterior distribution of {Ψ k} k=1 N Furthermore, the covariance calculation unit 13 calculates the following equation (23):

[0124]

number

[0125] By the hidden variable z k Calculate the covariance parameter η of the prior distribution of , where N is the number of samples for each state variable as described above, and r is the covariance parameter η of the prior distribution of . k , and as an example, r=1.

[0126] (Step S14: Ratio parameter calculation process) Subsequently, in step S14, the ratio parameter calculation unit 14 calculates the target data TD expressed as the following equation (24):

[0127]

number

[0128] and the regression coefficient RC expressed as the following equation (25):

[0129]

number

[0130] and the hidden variable z k The covariance matrix of the posterior distribution of {Ψ k} k=1 N With reference to the above, the ratio parameter (internal division ratio parameter vector) RP expressed as the following equation (26) is

[0131]

number

[0132] As an example, the ratio parameter calculation unit 14 calculates (updates) the following equation (27):

[0133]

number

[0134] By μ k The ratio parameter calculation unit 14 calculates (updates) the ratio parameter RP by executing the process of calculating the following equation (28) under constraint conditions according to the correspondence indicated by the correspondence information CI.

[0135]

number

[0136] By μ k By performing the process of calculating the ratio parameter (internal division ratio parameter vector) RP expressed by the following equation (30), under the constraints (constraints) expressed by the following equations (29A) to (29C),

[0137]

number

[0138]

number

[0139] Here, the first equation of the constraint condition (29A) can be expressed by explicitly specifying the index (i) related to the model as follows: Σ i=1 m μ k (i) = 1 In other words, the first equation of the constraint condition equation (29A) indicates that the sum of the ratio parameter RP across the indexes related to the model is 1. Furthermore, the second equation of the constraint condition equation (29A) indicates that the value of the ratio parameter RP is equal to or greater than 0. In this way, by calculating the ratio parameter RP under the constraint condition equation (29A), the ratio parameter calculation unit 14 can suitably calculate the ratio parameter RP even when, for example, a normal distribution is adopted as the posterior distribution of the latent variable.

[0140] In addition, in the above constraint equation (29B), M i denotes the set of target data TD corresponding to the i-th endpoint model. In other words, M iindicates a set of target data TD that the correspondence relationship information CI indicates will be used as training data for the i-th end point model. Therefore, the above constraint condition equation (29B) indicates that the ratio parameter μ calculated using the j-th target data TD from the set of target data TD that indicates will be used as training data for the i-th end point model. j The sum of these ratio parameters RP is 1. That is, the ratio parameter calculation unit 14 calculates the ratio parameters under the constraint condition according to the correspondence relationship, which is a constraint condition on the sum of the ratio parameters RP, by using the above constraint condition formula (29B). On the other hand, the above constraint condition formula (29C) calculates the ratio parameters μ in the end point model that does not use the j-th target data TD as training data. h indicates that ∇ ...

[0141] For example, consider a model that predicts sales of drinking water in a store. As an example, the endpoint models are (1) Model 1, which predicts sales when a measure is implemented, (2) Model 2, which predicts sales when the content volume is changed, (3) Model 3, which predicts sales when the appearance is changed, (4) Model 4, which predicts sales when the external environment changes, and Model 5, which predicts sales under all of the situations (1) to (5).

[0142] As an example of this case, if the target data TD is data on sales when a measure is implemented, the correspondence relationship information CI indicates that the target data TD is used as learning data for Model 1 and Model 5.

[0143] As described above, the ratio parameter calculation unit 14 may calculate the ratio parameter RP using an update equation including a normalization term according to the correspondence relationship. For example, the ratio parameter calculation unit 14 calculates the ratio parameter RP using the following equations (31A) and (31B).

[0144]

number

[0145] Of the equations (31A), the following equation (32) is a normalization term according to the correspondence relationship.

[0146]

number

[0147] In equation (31A), γ k is a coefficient indicating the strength of regularization. That is, equation (32) is the ratio parameter μ calculated using the k-th target data TD. k is the ratio parameter μ calculated using target data TD similar to the k-th target data TD k’ This indicates that the value of the ratio parameter RP is close to the value of the target data TD. With this configuration, when two pieces of target data TD are similar to each other and the ratio parameter RP is calculated using each of the two pieces of target data TD, the ratio parameter calculation unit 14 calculates a ratio parameter RP that is close in value. Therefore, the ratio parameter calculation unit 14 can calculate a ratio parameter RP with improved interpretability.

[0148] (Step S17: Convergence determination process) Subsequently, in step S17, the convergence determination unit 17 determines whether or not the series of processes in the above-mentioned steps S12, S13, and S14 have converged. This may be expressed as determining whether or not the above-mentioned variational Bayes EM algorithm has converged, or as determining whether or not the calculation related to the ratio parameter RP in step S14 has converged. As an example, the convergence determination unit 17 determines whether or not the variational lower bound (VLB) J obtained by the following formula (33),

[0149]

number

[0150] and determines that the series of processes in steps S12, S13, and S14 described above have converged if the change in the variation lower limit is equal to or less than a predetermined threshold. For example, in the nth iteration of the series of processes including steps S12, S13, and S14 described above, the convergence determination unit 17 compares the (n-1)th variation lower limit with the nth variation lower limit in the convergence determination process, and determines that the series of processes in steps S12, S13, and S14 described above have converged if the absolute value of the difference between them is equal to or less than a predetermined threshold.

[0151] (Step S15: Output process) If the convergence determination unit 17 determines that "convergence has occurred" in step S17, then in step S15 the output unit 15 outputs the regression coefficient RC calculated by the regression coefficient calculation unit 12 in step S12 and the ratio parameter RP (learning result LR) calculated by the ratio parameter calculation unit 14 in step S14. In this way, if the convergence determination unit 17 determines that the calculation related to the ratio parameter RP has "converged," the output unit 15 outputs the learning result LR, thereby making it possible to output a suitable learning result LR.

[0152] In step S15, the output unit 15 outputs the hidden variable z calculated by the covariance calculation unit 13 in step S13. k The covariance parameter η of the prior distribution of the hidden variable z k The output unit 15 may be configured to further output the covariance matrix Ψ of the posterior distribution of the hidden variable z k The covariance parameter η of the prior distribution of and the hidden variable z k The covariance matrix Ψ of the posterior distribution of σ can be presented to the user.

[0153] In step S15, the output unit 15 may be configured to display graphs defined by the regression coefficients RC for at least two of the plurality of target models in a manner that allows them to be distinguished from one another.

[0154] 8 shows an example of a graph that the output unit 15 displays via the input / output unit 17A in this step. In the example shown in FIG. 8, the output unit 15 displays the regression coefficients RC calculated for each of the plurality of target models, which are expressed as the following equation (34), in the regression coefficient calculation process in step S12.

[0155]

number

[0156] Among them, the regression coefficient W of Model 1 (1) The graph L1 defined by the regression coefficient W of Model 2 (2) and graph L2 defined by the above are displayed so as to be distinguishable from each other. In this way, according to the information processing device 1A according to this exemplary embodiment, a plurality of models are used and the ratio parameter RP of each model can be determined by learning, so that an output result having a range (as an example, an output result having a range defined by the above graph L1 and graph L2) can be generated.

[0157] 8, the output unit 15 may refer to the correspondence relationship information CI and display what models the graph L1 and the graph L2 correspond to. In Fig. 8, the output unit 15 displays that the graph L1 is a model corresponding to the situation A, and the graph L2 is a model corresponding to the situation B.

[0158] (Example of processing executed by the prediction unit 18) The prediction unit 18 derives multiple prediction results PR by applying the regression coefficients RC for each of the multiple target models and the ratio parameters RP calculated by the ratio parameter calculation unit 14, which are determined according to the type of the inference data PD, to the inference data PD. As an example, when the inference data PD includes a feature amount (explanatory variable) x0, the prediction unit 18 derives the prediction result PR by applying the regression coefficients RC for each of the multiple target models and the ratio parameters RP determined according to the type of the inference data PD to the feature amount x0. In this case, an example of a graph including the prediction results RP displayed by the output unit 15 via the input / output unit 17A is shown in FIG. 9. FIG. 9 shows another example of a graph displayed by the output unit 15 via the input / output unit 17A.

[0159] In addition, the ratio parameter determined according to the type of inference data PD refers, as an example, to a ratio parameter learned (updated) by the above-mentioned learning process using learning data (target data) associated with a type that is the same as or similar to the type of the inference data PD, but this does not limit this exemplary embodiment.

[0160] For example, assume that each of the multiple target data TD is sales of drinking water at multiple stores. Also, a situation in which a campaign for the drinking water is being conducted is defined as situation A, and a situation in which the volume of the drinking water has been increased is defined as situation B. Examples of situations include, but are not limited to, a situation in which the appearance of the drinking water container has been changed, a situation in which a new store has opened near the store, a situation in which it is raining, and a situation in which all of these situations are included.

[0161] For example, when predicting sales of a drink at a certain store at a certain date and time x0, where a campaign for the drink is being carried out and the volume of the drink has been increased, the prediction unit 18 calculates the regression coefficient W of model 1 corresponding to situation A at the certain date and time x0. (1) The prediction result P1 is derived by applying the regression coefficient W of Model 2 corresponding to situation B at a certain date and time x0. (2)The prediction unit 18 derives the prediction result P2 by applying the ratio parameter μ (1) and the ratio parameter μ of Model 2 (2) The prediction result RP is derived by applying

[0162] In this way, the information processing device 1A according to this exemplary embodiment can generate a prediction result PR according to the type of inference data PD. Therefore, the information processing device 1A according to this exemplary embodiment can present a prediction result PR that has favorable explainability to the user.

[0163] 10, the output unit 15 may display a graph defined by the regression coefficients R of the respective models. FIG. 10 shows another example of a graph displayed by the output unit 15 via the input / output unit 17A. The output unit 15 displays a graph L1 defined by the regression coefficients R of model 1 corresponding to situation A, a graph L2 defined by the regression coefficients R of model 2 corresponding to situation B, and a graph L3 defined by the regression coefficients R of model 3 corresponding to situation C in a manner that allows them to be distinguished from one another. Situation C may include at least one of situations A and B, or may be a situation different from situations A and B. According to the information processing device 1A according to this exemplary embodiment, even with the above-described configuration, it is possible to present a prediction result PR having favorable explainability to the user.

[0164] (Effects of information processing device 1A) As described above, the information processing device 1A calculates the regression coefficient RC for each of the plurality of target models by referring to the ratio parameter RP that defines the ratio of the plurality of target models and the target data TD. Also, in the information processing device 1A, the target data TD, the ratio parameter RP, the regression coefficient RC, and the hidden variable z k With reference to the prior distribution of the hidden variable z k The covariance parameters of the prior distribution of and the hidden variable z kIn the information processing device 1A, the target data TD, the regression coefficient RC, and the latent variable z k The ratio parameter RP is calculated by referring to the covariance matrix of the posterior distribution of the regression coefficient RC. Furthermore, the information processing device 1A stores the regression coefficient RC and the calculated ratio parameter RP in association with the correspondence between the target data TD and a plurality of target models.

[0165] With this configuration, the information processing device 1A can preferably improve the explainability by using information on the correspondence relationship in an optimization method that refers to a plurality of models.

[0166] Third Exemplary Embodiment A third exemplary embodiment, which is an example of an embodiment of the present invention, will be described in detail with reference to the drawings. Components having the same functions as those described in the above exemplary embodiment will be assigned the same reference numerals, and their description will be omitted as appropriate. The scope of application of each technical means employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technical means employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise. Furthermore, each technical means shown in each drawing referenced to explain this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise.

[0167] (Configuration of information processing device 2A) The configuration of the information processing device 2A will be described with reference to Fig. 11. Fig. 11 is a block diagram showing the configuration of the information processing device 2A. As shown in Fig. 11, the information processing device 2A includes a control unit 20A, a storage unit 25A, a communication unit 26A, and an input / output unit 27A.

[0168] The storage unit 25A stores data referenced by the control unit 20A, similar to the storage unit 15A described above. Examples of data stored in the storage unit 25A include, but are not limited to, inference data PD, correspondence information CI, learning results LR, and prediction results PR, as shown in Fig. 11. The inference data PD, correspondence information CI, learning results LR, and prediction results PR are as described above.

[0169] The communication unit 26A is an interface that transmits and receives data via a network, similar to the above-described communication unit 16A.

[0170] The input / output unit 27A is an interface that receives input of data and outputs data, similar to the above-described input / output unit 17A.

[0171] (Control unit 20A) The control unit 20A controls each component included in the information processing device 2 A. The control unit 20A also includes an acquisition unit 21, a prediction unit 22, and an output unit 23, as shown in FIG.

[0172] The acquisition unit 21 acquires the inference data PD and the correspondence relationship information CI, and stores the acquired inference data PD and correspondence relationship information CI in the storage unit 25A.

[0173] The prediction unit 22 derives a prediction result PR by applying the regression coefficients RC for each of the plurality of target models and the ratio parameters RP calculated by the ratio parameter calculation unit 14, which are determined according to the type of the inference data PD, to the inference data PD. The regression coefficients RC and ratio parameters RP applied by the prediction unit 22 are included in the learning result LR. The prediction unit 22 stores the prediction result PR in the memory unit 25A.

[0174] The regression coefficients RC and ratio parameters RP applied by the prediction unit 22 are the regression coefficients RC and ratio parameters RP learned by a learning process including a regression coefficient calculation process that calculates the regression coefficients RC for each of a plurality of target models by referring to the ratio parameters RP and learning data, and a ratio parameter calculation process that calculates the ratio parameters RP by referring to the learning data and the regression coefficients RC. An example of the learning data is the target data TD described above.

[0175] The output unit 23 outputs the prediction result PR by the prediction unit 22. Output by the output unit 23. Examples of the prediction result PR output by the output unit 23 are as described with reference to FIGS.

[0176] (Effects of information processing device 2A) As described above, in the information processing device 2A, the prediction result PR is derived by applying the regression coefficient RC for each of the multiple target models and the ratio parameters RP calculated by the ratio parameter calculation unit 14, which are determined according to the type of the inference data PD, to the inference data PD.

[0177] With this configuration, the information processing device 2A can generate a prediction result PR according to the type of the inference data PD.

[0178] Fourth Exemplary Embodiment A fourth exemplary embodiment, which is an example of an embodiment of the present invention, will be described in detail with reference to the drawings. Components having the same functions as those described in the above exemplary embodiment will be assigned the same reference numerals, and their description will be omitted as appropriate. The scope of application of each technical means employed in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technical means employed in this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise. Furthermore, each technical means shown in each drawing referenced to explain this exemplary embodiment can also be employed in other exemplary embodiments included in the present disclosure, to the extent that no particular technical obstacles arise.

[0179] (Configuration of information processing device 1B) The configuration of the information processing device 1B will be described with reference to Fig. 12. Fig. 12 is a block diagram showing the configuration of the information processing device 1B. As shown in Fig. 12, the information processing device 1B includes a control unit 10B, a storage unit 15B, a communication unit 16A, and an input / output unit 17A. The communication unit 16A and the input / output unit 17A have been described above, and therefore description thereof will be omitted.

[0180] Similar to the above-described storage unit 15A, the storage unit 15B stores data referenced by the control unit 10B. Examples of data stored in the storage unit 15B include, but are not limited to, target data TD, ratio parameters RP, regression coefficients RC, correspondence information CI, learning results LR, inference data PD, and prediction results PR, as shown in Fig. 12. The target data TD, ratio parameters RP, regression coefficients RC, correspondence information CI, learning results LR, inference data PD, and prediction results PR are as described above, and therefore will not be described here.

[0181] (control unit 10B) The control unit 10B controls each component included in the information processing device 1B, similar to the control unit 10A described above. As shown in FIG. 12 , the control unit 10B includes an acquisition unit 11, a regression coefficient calculation unit 12, a ratio parameter calculation unit 14, an output unit 15, an initial value determination unit 16, a convergence determination unit 17, and a prediction unit 18. In this exemplary embodiment, the acquisition unit 11, the regression coefficient calculation unit 12, the ratio parameter calculation unit 14, the initial value determination unit 16, the convergence determination unit 17, and the prediction unit 18 respectively implement an acquisition unit, a regression coefficient calculation unit, a ratio parameter calculation unit, an initial value determination unit, a convergence determination unit, and a prediction unit. The output unit 15 implements a storage unit and an output unit. The acquisition unit 11, the output unit 15, the initial value determination unit 16, the convergence determination unit 17, and the prediction unit 18 are as described above, and therefore will not be described further.

[0182] The regression coefficient calculation unit 12 calculates a regression coefficient RC for each of the plurality of target models by referring to the ratio parameter RP that defines the ratio of the plurality of target models and the target data TD. In this exemplary embodiment, the regression coefficient calculation unit 12 calculates the regression coefficient RC for each of the plurality of target models by referring to the hidden variable z k The covariance parameter η of the prior distribution of and the hidden variable z k The regression coefficients RC are calculated using the least squares method without updating the covariance matrices Φ and Φ of the prior distributions.

[0183] The ratio parameter calculation unit 14 calculates the ratio parameter RP by referring to the target data TD and the regression coefficient RC. In this exemplary embodiment, the ratio parameter RP is not limited to an internal ratio parameter, and may be an external ratio parameter.

[0184] (Example of processing flow in information processing device 1B) 13 is a diagram showing an example of the flow of processing in the information processing device 1B according to this exemplary embodiment. The acquisition processing S11, the initial value determination processing S16, and the output processing S15 are as described above, and therefore descriptions thereof will be omitted.

[0185] (Step S12: Regression coefficient calculation process) In step S12, the regression coefficient calculation unit 12 calculates the ratio parameter RP expressed as the following equation (35):

[0186]

number

[0187] and the target data TD expressed as the following equation (36):

[0188]

number

[0189] and the regression coefficient RC for each of the plurality of target models, which is expressed as the following equation (37):

[0190]

number

[0191] As an example, the regression coefficient calculation unit 12 calculates the regression coefficient W that minimizes the value of the evaluation function of the following equation (38) with the value of the ratio parameter RP and the value of the target data TD set as fixed values. (i) Calculate the value of

[0192]

number

[0193] That is, the regression coefficient calculation unit 12 calculates the regression coefficient W using the least squares method.

[0194] (Step S14: Ratio parameter calculation process) Subsequently, in step S14, the ratio parameter calculation unit 14 calculates the target data TD expressed as the following equation (39):

[0195]

number

[0196] and the regression coefficient RC expressed as the following equation (40):

[0197]

number

[0198] and the ratio parameter RP expressed as the following equation (41)

[0199]

number

[0200] As described above, the ratio parameter calculation unit 14 calculates (updates) the hidden variables z kThe covariance parameter η of the prior distribution of and the hidden variable z k The ratio parameter RP is calculated without updating the covariance matrix Φ of the prior distribution of . -1 The ratio parameter RP is calculated by the following equation (42) with ρ = 0:

[0201]

number

[0202] Furthermore, similarly to the above-described exemplary embodiment, the ratio parameter calculation unit 14 calculates the ratio parameter RP under the constraints expressed as the following equations (43A) and (43B).

[0203]

number

[0204] (Step S17: Convergence determination process) Subsequently, in step S17, the convergence determination unit 17 determines whether or not the series of processes in steps S12, S13, and S14 described above has converged. In step S17, the convergence determination unit 17, for example, determines the variational lower bound (VLB) J obtained by the following equation (44), as in the exemplary embodiment:

[0205]

number

[0206] If the change in the variation lower limit is equal to or less than a predetermined threshold, it is determined that the series of processes in steps S12, S13, and S14 have converged.

[0207] As another example, the convergence determination unit 17 determines whether the value of the ratio parameter RP has converged. For example, in the nth convergence determination process of repeating a series of processes including the above-mentioned steps S12, S13, and S14, the convergence determination unit 17 compares the value of the ratio parameter RP at the (n-1)th time with the value of the ratio parameter RP at the nth time, and determines that the series of processes at the above-mentioned steps S12, S13, and S14 has converged if the absolute value of the difference between them is equal to or smaller than a predetermined threshold.

[0208] (Effects of information processing device 1B) As described above, the information processing device 1B calculates the regression coefficient RC using the least squares method. With this configuration, the information processing device 1B can also suitably improve the explainability in an optimization method that refers to multiple models, similar to the information processing device 1A.

[0209] [Software implementation example] Some or all of the functions of the information processing devices 1, 1A, 1B, 2, and 2A (hereinafter also referred to as "each of the above devices") may be realized by hardware such as an integrated circuit (IC chip), or by software.

[0210] In the latter case, each of the above devices is realized by, for example, a computer that executes instructions of a program, which is software that realizes each function. An example of such a computer (hereinafter referred to as computer C) is shown in Figure 14. Figure 14 is a block diagram showing the hardware configuration of computer C that functions as each of the above devices.

[0211] The computer C includes at least one processor C1 and at least one memory C2. The memory C2 stores a program P for causing the computer C to operate as each of the above-mentioned devices. In the computer C, the processor C1 reads and executes the program P from the memory C2, thereby realizing the functions of each of the above-mentioned devices.

[0212] The processor C1 may be, for example, a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a tensor processing unit (TPU), a quantum processor, a microcontroller, or a combination thereof. The memory C2 may be, for example, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), or a combination thereof.

[0213] The computer C may further include a RAM (Random Access Memory) for expanding the program P during execution and for temporarily storing various data. The computer C may also include a communication interface for transmitting and receiving data to and from other devices. The computer C may also include an input / output interface for connecting input / output devices such as a keyboard, mouse, display, and printer.

[0214] Furthermore, the program P can be recorded on a non-transitory tangible recording medium M that can be read by the computer C. Such a recording medium M can be, for example, a tape, a disk, a card, a semiconductor memory, or a programmable logic circuit. The computer C can acquire the program P via such a recording medium M. The program P can also be transmitted via a transmission medium. Such a transmission medium can be, for example, a communication network or broadcast waves. The computer C can also acquire the program P via such a transmission medium.

[0215] [Appendix A] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0216] (Appendix A1) an acquisition means for acquiring information regarding target data, the number of models of a plurality of target models, and a correspondence relationship between the target data and the plurality of target models; a regression coefficient calculation means for calculating a regression coefficient for each of the plurality of target models by referring to a ratio parameter that defines a ratio of the plurality of target models and the target data; a ratio parameter calculation means for calculating the ratio parameters by referring to the target data and the regression coefficients; a storage means for storing the regression coefficients and the ratio parameters calculated by the ratio parameter calculation means in association with the correspondence relationship; An information processing device comprising:

[0217] (Appendix A2) The ratio parameter calculation means calculates the ratio parameter under a constraint condition according to the correspondence relationship. 10. The information processing device according to claim 1,

[0218] (Appendix A3) The ratio parameter calculation means calculates the ratio parameter under a constraint on the sum of the ratio parameters, the constraint corresponding to the correspondence relationship. 10. The information processing device according to claim 9, wherein the information processing device is a

[0219] (Appendix A4) The ratio parameter calculation means calculates the ratio parameter using an update formula including a normalization term according to the correspondence relationship. 10. The information processing device according to claim 9, wherein the information processing device is a device for processing information according to claim 1.

[0220] (Appendix A5) an output means for outputting the regression coefficients calculated by the regression coefficient calculation means and the ratio parameters calculated by the ratio parameter calculation means; It also has An information processing device according to any one of appendices A1 to A4.

[0221] (Appendix A6) The output means displays graphs defined by the regression coefficients for at least two of the plurality of target models in a manner that allows them to be distinguished from one another. 10. The information processing device according to claim 9, wherein the information processing device is a

[0222] (Appendix A7) The obtaining means further obtains information regarding a prior distribution of the latent variables; The information processing device includes: Further comprising a covariance calculation means for calculating a covariance parameter of the prior distribution of the latent variables and a covariance matrix of the posterior distribution of the latent variables by referring to the target data, the ratio parameter, the regression coefficient, and information on the prior distribution of the latent variables; The ratio parameter calculation means calculates the ratio parameter by further referring to a covariance matrix of the posterior distribution of the hidden variables. An information processing device according to any one of appendices A1 to A6.

[0223] (Appendix A8) The method further includes an initial value determining means for determining an initial value of the ratio parameter to be referenced by the regression coefficient calculating means. 10. The information processing device according to claim 7,

[0224] (Appendix A9) further comprising a convergence determination means for determining whether or not the calculation regarding the ratio parameter has converged; The storage means stores the regression coefficients and the ratio parameters when the convergence determination means determines that convergence has occurred. An information processing device according to appendix A7 or A8.

[0225] (Appendix A10) the acquiring means further acquires inference data and information regarding the type of the inference data; The information processing device includes: The inference data includes: the regression coefficients for each of the plurality of target models; Among the ratio parameters calculated by the ratio parameter calculation means, a ratio parameter determined according to the type of the inference data is A prediction method that derives a prediction result by applying It also has An information processing device according to any one of appendices A1 to A9.

[0226] (Appendix A11) an acquisition means for acquiring inference data and information regarding the type of the inference data; The inference data includes: regression coefficients for each of a plurality of target models; a ratio parameter that defines a ratio of the plurality of target models, the ratio parameter being determined according to the type of the inference data; a prediction means for deriving a prediction result by applying an output means for outputting a prediction result by the prediction means; Equipped with The regression coefficients and the ratio parameters are a regression coefficient calculation process that calculates a regression coefficient for each of a plurality of target models by referring to the ratio parameters and the learning data; and A ratio parameter calculation process for calculating the ratio parameters by referring to the learning data and the regression coefficients. The information processing device is a regression coefficient and a ratio parameter learned by a learning process including the steps of:

[0227] (Appendix A12) A program for causing a computer to function as the information processing device according to any one of Appendix A1 to Appendix A10, the program causing a computer to function as the acquisition means, the regression coefficient calculation means, the ratio parameter calculation means, and the storage means.

[0228] (Appendix A13) A program for causing a computer to function as the information processing device described in Appendix A11, the program causing a computer to function as the acquisition means, the prediction means, and the output means.

[0229] [Appendix B] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0230] (Appendix B1) At least one processor an acquisition process for acquiring information about target data, the number of models of a plurality of target models, and a correspondence relationship between the target data and the plurality of target models; a regression coefficient calculation process that calculates a regression coefficient for each of the plurality of target models by referring to a ratio parameter that defines a ratio of the plurality of target models and the target data; a ratio parameter calculation process for calculating the ratio parameters by referring to the target data and the regression coefficients; a storage process of storing the regression coefficients and the ratio parameters calculated in the ratio parameter calculation process in association with the correspondence relationship; An information processing method comprising:

[0231] (Appendix B2) In the ratio parameter calculation process, the at least one processor calculates the ratio parameter under a constraint condition according to the correspondence relationship. 1. The information processing method described in Appendix B1.

[0232] (Appendix B3) In the ratio parameter calculation process, the at least one processor calculates the ratio parameter under a constraint on the sum of the ratio parameters, the constraint being in accordance with the correspondence relationship. 1. The information processing method described in Appendix B2.

[0233] (Appendix B4) In the ratio parameter calculation process, the at least one processor calculates the ratio parameter using an update equation including a normalization term according to the correspondence relationship. 1. An information processing method as described in Appendix B2 or B3.

[0234] (Appendix B5) an output process in which the at least one processor outputs the regression coefficients calculated by the regression coefficient calculation process and the ratio parameters calculated by the ratio parameter calculation process; Further includes 1. An information processing method according to any one of appendices B1 to B4.

[0235] (Appendix B6) In the output process, the at least one processor displays graphs defined by the regression coefficients for at least two of the plurality of target models in a manner that allows them to be distinguished from one another. The information processing method described in Appendix B5.

[0236] (Appendix B7) In the obtaining process, the at least one processor further obtains information regarding a prior distribution of a hidden variable; The at least one processor further includes a covariance calculation process for calculating covariance parameters of the prior distribution of the latent variables and a covariance matrix of the posterior distribution of the latent variables by referring to the target data, the ratio parameters, the regression coefficients, and information on the prior distribution of the latent variables; In the ratio parameter calculation process, the at least one processor further refers to a covariance matrix of the posterior distribution of the hidden variables to calculate the ratio parameter. 10. An information processing method according to any one of appendices B1 to B6.

[0237] (Appendix B8) The at least one processor further includes an initial value determination process for determining an initial value of the ratio parameter referenced by the regression coefficient calculation process. An information processing method as described in Appendix B7.

[0238] (Appendix B9) The at least one processor further includes a convergence determination process for determining whether or not the calculation regarding the ratio parameter has converged; In the storage process, the at least one processor stores the regression coefficients and the ratio parameters when it is determined that the convergence determination process has converged. An information processing method as described in Appendix B7 or B8.

[0239] (Appendix B10) The acquisition process further acquires inference data and information regarding the type of the inference data, the at least one processor: The inference data includes: the regression coefficients for each of the plurality of target models; Among the ratio parameters calculated by the ratio parameter calculation process, a ratio parameter determined according to the type of the inference data is A prediction process that derives a prediction result by applying Further includes 10. An information processing method according to any one of appendices B1 to B9.

[0240] (Appendix B11) At least one processor an acquisition process for acquiring inference data and information regarding the type of the inference data; The inference data includes: regression coefficients for each of a plurality of target models; a ratio parameter that defines a ratio of the plurality of target models, the ratio parameter being determined according to the type of the inference data; a prediction process for deriving a prediction result by applying an output process for outputting a prediction result in the prediction process; Including, The regression coefficients and the ratio parameters are a regression coefficient calculation process that calculates a regression coefficient for each of a plurality of target models by referring to the ratio parameters and the learning data; and A ratio parameter calculation process for calculating the ratio parameters by referring to the learning data and the regression coefficients. The information processing method is a regression coefficient and a ratio parameter learned by a learning process including:

[0241] [Appendix C] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0242] (Appendix C1) at least one processor, an acquisition process for acquiring information about target data, the number of models of a plurality of target models, and a correspondence relationship between the target data and the plurality of target models; a regression coefficient calculation process that calculates a regression coefficient for each of the plurality of target models by referring to a ratio parameter that defines a ratio of the plurality of target models and the target data; a ratio parameter calculation process for calculating the ratio parameters by referring to the target data and the regression coefficients; a storage process of storing the regression coefficients and the ratio parameters calculated in the ratio parameter calculation process in association with the correspondence relationship; An information processing device that executes the above.

[0243] The information processing device may further include a memory, and the memory may store a program for causing the at least one processor to execute each of the processes.

[0244] (Appendix C2) In the ratio parameter calculation process, the at least one processor calculates the ratio parameter under a constraint condition according to the correspondence relationship. 10. The information processing device according to claim 9, wherein the information processing device is a device for processing information according to claim 1.

[0245] (Appendix C3) In the ratio parameter calculation process, the at least one processor calculates the ratio parameter under a constraint on the sum of the ratio parameters, the constraint being in accordance with the correspondence relationship. 10. The information processing device according to claim 9, wherein the information processing device is a

[0246] (Appendix C4) In the ratio parameter calculation process, the at least one processor calculates the ratio parameter using an update equation including a normalization term according to the correspondence relationship. 10. The information processing device according to claim 9, wherein the information processing device is a device for processing information according to claim 1.

[0247] (Appendix C5) the at least one processor: an output process for outputting the regression coefficients calculated by the regression coefficient calculation process and the ratio parameters calculated by the ratio parameter calculation process; Run the following again: An information processing device according to any one of appendices C1 to D4.

[0248] (Appendix C6) In the output process, the at least one processor displays graphs defined by the regression coefficients for at least two of the plurality of target models in a manner that allows them to be distinguished from one another. 10. The information processing device according to claim 9, wherein the information processing device is a device for processing information.

[0249] (Appendix C7) In the obtaining process, the at least one processor further obtains information regarding a prior distribution of a hidden variable; The information processing device includes: the at least one processor: Further performing a covariance calculation process to calculate a covariance parameter of the prior distribution of the latent variable and a covariance matrix of the posterior distribution of the latent variable by referring to the target data, the ratio parameter, the regression coefficient, and information on the prior distribution of the latent variable; In the ratio parameter calculation process, the at least one processor further refers to a covariance matrix of the posterior distribution of the hidden variables to calculate the ratio parameter. An information processing device according to any one of appendices C1 to D6.

[0250] (Appendix C8) the at least one processor: An initial value determination process is further performed to determine the initial value of the ratio parameter referred to in the regression coefficient calculation process. 10. The information processing device according to claim 7,

[0251] (Appendix C9) the at least one processor: further performing a convergence determination process to determine whether or not the calculation regarding the ratio parameter has converged; In the storage process, the at least one processor stores the regression coefficients and the ratio parameters when it is determined that the convergence determination process has converged. An information processing device according to appendix C7 or D8.

[0252] (Appendix C10) The acquisition process further acquires inference data and information regarding the type of the inference data, The information processing device includes: The inference data includes: the regression coefficients for each of the plurality of target models; Among the ratio parameters calculated by the ratio parameter calculation process, a ratio parameter determined according to the type of the inference data is the at least one processor: A prediction process that derives a prediction result by applying Run the following again: An information processing device according to any one of appendices C1 to D9.

[0253] (Appendix C11) The at least one processor: an acquisition process for acquiring inference data and information regarding the type of the inference data; The inference data includes: regression coefficients for each of a plurality of target models; a ratio parameter that defines a ratio of the plurality of target models, the ratio parameter being determined according to the type of the inference data; a prediction process for deriving a prediction result by applying an output process for outputting a prediction result obtained by the prediction process; Prepare, execute, The regression coefficients and the ratio parameters are a regression coefficient calculation process that calculates a regression coefficient for each of a plurality of target models by referring to the ratio parameters and the learning data; and A ratio parameter calculation process for calculating the ratio parameters by referring to the learning data and the regression coefficients. The information processing device is configured to process the regression coefficients and ratio parameters learned by a learning process that executes the above.

[0254] [Appendix D] This disclosure includes the techniques described in the following appendices. However, the present invention is not limited to the techniques described in the following appendices, and various modifications are possible within the scope of the claims.

[0255] (Appendix D1) A non-transitory recording medium on which a program that causes a computer to function as an information processing device is recorded, The program causes the computer to: an acquisition process for acquiring information about target data, the number of models of a plurality of target models, and a correspondence relationship between the target data and the plurality of target models; a regression coefficient calculation process that calculates a regression coefficient for each of the plurality of target models by referring to a ratio parameter that defines a ratio of the plurality of target models and the target data; a ratio parameter calculation process for calculating the ratio parameters by referring to the target data and the regression coefficients; a storage process of storing the regression coefficients and the ratio parameters calculated in the ratio parameter calculation process in association with the correspondence relationship; A non-transitory recording medium on which a program for executing a program is recorded.

[0256] (Appendix D2) A non-transitory recording medium on which a program that causes a computer to function as an information processing device is recorded, The program causes the computer to: an acquisition process for acquiring inference data and information regarding the type of the inference data; The inference data includes: regression coefficients for each of a plurality of target models; a ratio parameter that defines a ratio of the plurality of target models, the ratio parameter being determined according to the type of the inference data; a prediction process for deriving a prediction result by applying an output process for outputting a prediction result in the prediction process; Execute The regression coefficients and the ratio parameters are a regression coefficient calculation process that calculates a regression coefficient for each of a plurality of target models by referring to the ratio parameters and the learning data; and A ratio parameter calculation process for calculating the ratio parameters by referring to the learning data and the regression coefficients. are the regression coefficients and ratio parameters learned by the learning process including A non-transitory recording medium on which a program is recorded. [Explanation of symbols]

[0257] 1, 1A, 1B, 2, 2A Information processing device 11, 21 Acquisition Department 12 Regression coefficient calculation section 13 Covariance calculation part 14 Ratio parameter calculation section 15, 23 Output section 16 Initial value determination section 17 Convergence judgment section 18, 22 Prediction Section TD Target Data RP ratio parameter RC regression coefficient CI correspondence information DI distribution information LR learning results PD inference data PR prediction results

Claims

1. an acquisition means for acquiring information regarding target data, the number of models of a plurality of target models, and a correspondence relationship between the target data and the plurality of target models; a regression coefficient calculation means for calculating a regression coefficient for each of the plurality of target models by referring to a ratio parameter that defines a ratio of the plurality of target models and the target data; a ratio parameter calculation means for calculating the ratio parameters by referring to the target data and the regression coefficients; a storage means for storing the regression coefficients and the ratio parameters calculated by the ratio parameter calculation means in association with the correspondence relationship; An information processing device comprising:

2. The ratio parameter calculation means calculates the ratio parameter under a constraint condition according to the correspondence relationship. The information processing device according to claim 1 .

3. The ratio parameter calculation means calculates the ratio parameter under a constraint on the sum of the ratio parameters, the constraint corresponding to the correspondence relationship. The information processing device according to claim 2 .

4. further comprising an output means for outputting the regression coefficients calculated by the regression coefficient calculation means and the ratio parameters calculated by the ratio parameter calculation means, The output means displays graphs defined by the regression coefficients for at least two of the plurality of target models in a manner that allows them to be distinguished from one another. The information processing device according to claim 1 .

5. The obtaining means further obtains information regarding a prior distribution of the latent variables; The information processing device includes: Further comprising a covariance calculation means for calculating a covariance parameter of the prior distribution of the latent variables and a covariance matrix of the posterior distribution of the latent variables by referring to the target data, the ratio parameter, the regression coefficient, and information on the prior distribution of the latent variables; The ratio parameter calculation means calculates the ratio parameter by further referring to a covariance matrix of the posterior distribution of the hidden variables. The information processing device according to claim 1 .

6. further comprising a convergence determination means for determining whether or not the calculation regarding the ratio parameter has converged; The storage means stores the regression coefficients and the ratio parameters when the convergence determination means determines that convergence has occurred. The information processing device according to claim 1 .

7. the acquiring means further acquires inference data and information regarding the type of the inference data; The information processing device includes: The inference data includes: the regression coefficients for each of the plurality of target models; Among the ratio parameters calculated by the ratio parameter calculation means, a ratio parameter determined according to the type of the inference data is A prediction method that derives a prediction result by applying It also has The information processing device according to claim 1 .

8. an acquisition means for acquiring inference data and information regarding the type of the inference data; The inference data includes: regression coefficients for each of a plurality of target models; a ratio parameter that defines a ratio of the plurality of target models, the ratio parameter being determined according to the type of the inference data; a prediction means for deriving a prediction result by applying an output means for outputting a prediction result by the prediction means; Equipped with The regression coefficients and the ratio parameters are a regression coefficient calculation process that calculates a regression coefficient for each of a plurality of target models by referring to the ratio parameters and the learning data; and A ratio parameter calculation process for calculating the ratio parameters by referring to the learning data and the regression coefficients. The information processing device is configured to process the regression coefficients and ratio parameters learned by a learning process including the steps of:

9. At least one processor an acquisition process for acquiring information regarding target data, the number of models of a plurality of target models, and a correspondence relationship between the target data and the plurality of target models; a regression coefficient calculation process for calculating a regression coefficient for each of the plurality of target models by referring to a ratio parameter that defines a ratio of the plurality of target models and the target data; a ratio parameter calculation process for calculating the ratio parameters by referring to the target data and the regression coefficients; a storage process of storing the regression coefficients and the ratio parameters calculated in the ratio parameter calculation process in association with the correspondence relationship; An information processing method comprising:

10. 2. A program for causing a computer to function as the information processing device according to claim 1, the program causing a computer to function as the acquisition means, the regression coefficient calculation means, the ratio parameter calculation means, and the storage means.

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