Information processing apparatus, information processing method, and program

Abstract

To provide an information processing apparatus that can set an interior division ratio parameter with high accuracy.SOLUTION: An information processing apparatus comprises: an acquisition unit that acquires target data, the number of a plurality of target models, and information on the prior distribution of hidden variables; a regression coefficient calculation unit that calculates a regression coefficient for each of the plurality of target models; a covariance calculation unit that calculates a covariance parameter for the prior distribution of hidden variables and a covariance matrix of the posterior distribution of hidden variables; an interior division ratio parameter calculation unit that calculates an interior division ratio parameter; and an output unit that outputs the regression coefficients and the interior division ratio parameter.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] Techniques relating to Linear Parameter-Varying (LPV) models are known.

[0003] For example, Patent Document 1 discloses a method for describing a plant using an LPV model and configuring a controller for controlling the plant. In the method described in Patent Document 1, the ν gap is calculated for each candidate of multiple scheduling parameters, and scheduling parameter candidates are selected in descending order of the difference between the ν gaps for each candidate. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-113676 Summary of the Invention [Problem to be solved by the invention]

[0005] In the LVP model, it is preferable to set highly accurate scheduling parameters (hereinafter also referred to as "internal division ratio parameters"), but the method described in Patent Document 1 has had problems in this regard.

[0006] The present disclosure has been made in view of the above-mentioned problems, and an exemplary purpose thereof is to provide a technique that enables setting an interior division ratio parameter with high accuracy. [Means for solving the problem]

[0007] 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 a prior distribution of hidden variables; a regression coefficient calculation means for calculating a regression coefficient for each of the plurality of target models by referring to the target data and an internal division ratio parameter that defines an internal division ratio of the plurality of target models; a covariance calculation means for calculating a covariance parameter of the prior distribution of the hidden variable and a covariance matrix of the posterior distribution of the hidden variable by referring to the target data, the internal division ratio parameter, the regression coefficient, and information regarding the prior distribution of the hidden variable; an internal division ratio parameter calculation means for calculating the internal division ratio parameter by referring to the target data, the regression coefficient, and the covariance matrix of the posterior distribution of the hidden variable; and an output means for outputting the regression coefficient and the internal division ratio parameter calculated by the internal division ratio parameter calculation means.

[0008] An information processing device according to an exemplary aspect of the present disclosure includes an acquisition means for acquiring data for inference, a prediction means for deriving a plurality of prediction results by applying regression coefficients for each of a plurality of target models to the data for inference, and an output means for outputting the prediction results by the prediction means, wherein the regression coefficients for each of the plurality of target models are regression coefficients learned by a learning process including: a regression coefficient calculation process for calculating a regression coefficient for each of the plurality of target models by referring to learning data, an interior division ratio parameter that defines an interior division ratio of the plurality of target models; a covariance calculation process for calculating a covariance parameter of a prior distribution of the hidden variable and a covariance matrix of a posterior distribution of the hidden variable by referring to the learning data, the interior division ratio parameter, the regression coefficients calculated by the regression coefficient calculation process, and information on the prior distribution of the hidden variable; and an interior division ratio parameter calculation process for calculating the interior division ratio parameter by referring to the learning data, the regression coefficients calculated by the regression coefficient calculation process, and the covariance matrix of the posterior distribution of the hidden variable.

[0009] 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 a prior distribution of hidden variables; a regression coefficient calculation process in which an interior division ratio parameter that defines an interior division ratio of the plurality of target models is referenced to the target data, and the target data, to calculate a regression coefficient for each of the plurality of target models; a covariance calculation process in which a covariance parameter of a prior distribution of the hidden variable and a covariance matrix of a posterior distribution of the hidden variable are referenced to the target data, the interior division ratio parameter, the regression coefficient, and information regarding the prior distribution of the hidden variable; an interior division ratio parameter calculation process in which the interior division ratio parameter is referenced to calculate the interior division ratio parameter, and the covariance matrix of the posterior distribution of the hidden variable; and an output process in which the regression coefficient and the interior division ratio parameter calculated in the interior division ratio parameter calculation process are output.

[0010] 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]

[0011] According to an exemplary aspect of the present disclosure, an exemplary effect is achieved in that it is possible to set an interior division ratio parameter with high accuracy. [Brief explanation of the drawings]

[0012] [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 outputs 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] 1 is a block diagram illustrating a configuration of an information processing device according to the present disclosure. [Figure 11] 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

[0013] 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.

[0014] 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.

[0015] (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 covariance calculation unit 13, an interior 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 covariance calculation unit 13, the interior ratio parameter calculation unit 14, and the output unit 15 respectively realize an acquisition means, a regression coefficient calculation means, a covariance calculation means, an interior ratio parameter calculation means, and an output means.

[0016] (Acquisition part 11) The acquisition unit 11 acquires target data, the number of models of multiple target models, and information on the prior distribution of hidden variables. The acquisition unit 11 supplies the acquired target data to the regression coefficient calculation unit 12, the covariance calculation unit 13, and the interior 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 the acquired information on the prior distribution of hidden variables to the covariance calculation unit 13.

[0017] The information on the prior distribution of the latent variables includes a covariance matrix of the prior distribution of the latent variables. The information on the prior distribution of the latent variables may also include covariance parameters of the prior distribution of the latent variables (hereinafter, sometimes referred to as "covariance parameters of model likelihood"). The information processing device 1 according to this exemplary embodiment calculates the interior division ratio parameters as expected values ​​of the latent variables.

[0018] (Regression coefficient calculation part 12) The regression coefficient calculation unit 12 calculates the regression coefficient for each of the plurality of target models by referring to the target data and the interior ratio parameters that define the interior ratios of the plurality of target models. The regression coefficient calculation unit 12 supplies the calculated regression coefficients to the covariance calculation unit 13, the interior ratio parameter calculation unit 14, and the output unit 15.

[0019] (Covariance calculation unit 13) The covariance calculation unit 13 calculates the covariance parameters of the prior distribution of the latent variables and the covariance matrix of the posterior distribution of the latent variables by referring to the target data, the interior ratio parameters, the regression coefficients, and information on the prior distribution of the latent variables. The covariance calculation unit 13 supplies the calculated covariance parameters of the prior distribution of the latent variables and the covariance matrix of the posterior distribution of the latent variables to the interior ratio parameter calculation unit 14.

[0020] (Internal division parameter calculation part 14) The internal ratio parameter calculation unit 14 calculates the internal ratio parameters by referring to the target data, regression coefficients, and the covariance matrix of the posterior distribution of the latent variables. As an example, the internal ratio parameter calculation unit 14 calculates the internal ratio parameters as expected values ​​of the latent variables. The internal ratio parameter calculation unit 14 supplies the calculated internal ratio parameters to the output unit 15.

[0021] (Output section 15) The output unit 15 outputs the regression coefficients and the interior ratio parameters calculated by the interior ratio parameter calculation unit 14. The regression coefficients and the interior ratio parameters output by the output unit 15 are, for example, stored in a storage unit (not shown) or provided to a device external to the information processing device 1 by an input / output unit (not shown).

[0022] (Effects of information processing device 1) As described above, the information processing device 1 is configured to include an acquisition unit 11 that acquires information on the target data, the number of models of multiple target models, and the prior distribution of the hidden variables, a regression coefficient calculation unit 12 that calculates a regression coefficient for each of the multiple target models by referring to the target data and an interior ratio parameter that defines the interior ratio of the multiple target models, a covariance calculation unit 13 that calculates a covariance parameter for the prior distribution of the hidden variables and a covariance matrix for the posterior distribution of the hidden variables by referring to the target data, the interior ratio parameter, the regression coefficient, and information on the prior distribution of the hidden variables, an interior ratio parameter calculation unit 14 that calculates the interior ratio parameter by referring to the target data, the regression coefficient, and the covariance matrix for the posterior distribution of the hidden variables, and an output unit 15 that outputs the regression coefficient and the interior ratio parameter calculated by the interior ratio parameter calculation unit 14. Therefore, the information processing device 1 has the effect of being able to set highly accurate interior ratio parameters.

[0023] (Flow of information processing method S1) The flow of information processing method S1 will be described with reference to Fig. 2. Fig. 2 is a flow diagram showing the flow of information processing method S1. As shown in Fig. 2, information processing method S1 includes an acquisition process S11, a regression coefficient calculation process S12, a covariance calculation process S13, an interior ratio parameter calculation process S14, and an output process S15.

[0024] (Acquisition process S11) In the acquisition process S11, the acquisition unit 11 acquires target data, the number of models of multiple target models, and information regarding the prior distribution of latent variables. The acquisition unit 11 supplies the acquired target data to the regression coefficient calculation unit 12, the covariance calculation unit 13, and the interior 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 the acquired information regarding the prior distribution of latent variables to the covariance calculation unit 13.

[0025] (Regression coefficient calculation process S12) In the regression coefficient calculation process S12, the regression coefficient calculation unit 12 calculates the regression coefficient for each of the plurality of target models by referring to the target data and the interior ratio parameters that define the interior ratios of the plurality of target models. The regression coefficient calculation unit 12 supplies the calculated regression coefficients to the covariance calculation unit 13, the interior ratio parameter calculation unit 14, and the output unit 15.

[0026] (Covariance calculation process S13) In the covariance calculation process S13, the covariance calculation unit 13 refers to the target data, the interior ratio parameters, the regression coefficients, and information on the prior distribution of the latent variables to calculate the covariance parameters of the prior distribution of the latent variables and the covariance matrix of the posterior distribution of the latent variables. The covariance calculation unit 13 supplies the calculated covariance parameters of the prior distribution of the latent variables and the covariance matrix of the posterior distribution of the latent variables to the interior ratio parameter calculation unit 14.

[0027] (Internal division ratio parameter calculation process S14) In the internal ratio parameter calculation process S14, the internal ratio parameter calculation unit 14 calculates the internal ratio parameters by referring to the target data, regression coefficients, and the covariance matrix of the posterior distribution of the latent variables. The internal ratio parameter calculation unit 14 supplies the calculated internal ratio parameters to the output unit 15.

[0028] (Output process S15) In the output process S15, the output unit 15 outputs the regression coefficients and the interior ratio parameters calculated in the interior ratio parameter calculation process S14.

[0029] (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 multiple target models, and the prior distribution of the hidden variables; a regression coefficient calculation process S12 in which the regression coefficient calculation unit 12 calculates a regression coefficient for each of the multiple target models by referring to the target data and the internal ratio parameters that define the internal ratios of the multiple target models; a covariance calculation process S13 in which the covariance calculation unit 13 calculates covariance parameters of the prior distribution of the hidden variables and a covariance matrix of the posterior distribution of the hidden variables by referring to the target data, the internal ratio parameters, the regression coefficients, and information regarding the prior distribution of the hidden variables; an internal ratio parameter calculation process S14 in which the internal ratio parameter calculation unit 14 calculates internal ratio parameters by referring to the target data, the regression coefficients, and the covariance matrix of the posterior distribution of the hidden variables; and an output process S15 in which the output unit 15 outputs the regression coefficients and the internal ratio parameters calculated in the internal ratio parameter calculation process S14. Therefore, according to the information processing method S1, the same effects as those of the information processing device 1 described above can be obtained.

[0030] (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.

[0031] The acquisition unit 21 acquires data for inference. The acquisition unit 21 supplies the acquired data for inference to the prediction unit 22.

[0032] The prediction unit 22 derives a plurality of prediction results by applying the regression coefficients for each of a plurality of target models to the inference data. The prediction unit 22 supplies the prediction results to the output unit 23.

[0033] The output unit 23 outputs the prediction result from the prediction unit 22.

[0034] Furthermore, the regression coefficients for each of the multiple target models are regression coefficients learned by a learning process including: a regression coefficient calculation process that calculates the regression coefficients for each of the multiple target models by referring to the learning data and an internal ratio parameter that defines the internal ratio of the multiple target models; a covariance calculation process that calculates the covariance parameters of the prior distribution of the latent variables and the covariance matrix of the posterior distribution of the latent variables by referring to the learning data, the internal ratio parameter, the regression coefficients calculated by the regression coefficient calculation process, and information on the prior distribution of the latent variables; and an internal ratio parameter calculation process that calculates the internal ratio parameters by referring to the learning data, the regression coefficients calculated by the regression coefficient calculation process, and the covariance matrix of the posterior distribution of the latent variables. As an example, the regression coefficients are regression coefficients calculated by the information processing device 1 according to this exemplary embodiment.

[0035] (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, a prediction unit 22 that derives multiple prediction results by applying regression coefficients for each of multiple target models to the inference data, and an output unit 23 that outputs the prediction results obtained by the prediction unit 22. The regression coefficients for each of the multiple target models are regression coefficients learned through a learning process that includes: a regression coefficient calculation process that calculates the regression coefficient for each of the multiple target models by referencing the training data, the internal ratio parameters, the regression coefficients calculated by the regression coefficient calculation process, and information on the prior distribution of the hidden variables, to calculate covariance parameters for the prior distribution of the hidden variables and the covariance matrix for the posterior distribution of the hidden variables; and an internal ratio parameter calculation process that calculates the internal ratio parameters by referencing the training data, the regression coefficients calculated by the regression coefficient calculation process, and the covariance matrix for the posterior distribution of the hidden variables. Therefore, the information processing device 2 can achieve the same effects as the information processing device 1 described above.

[0036] (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.

[0037] (Acquisition process S21) In the acquisition process S21, the acquisition unit 21 acquires data for inference. The acquisition unit 21 supplies the acquired data for inference to the prediction unit 22.

[0038] (Prediction process S22) In the prediction process S22, the prediction unit 22 derives a plurality of prediction results by applying the regression coefficients for each of a plurality of target models to the inference data. The prediction unit 22 supplies the prediction results to the output unit 23.

[0039] (Output process S23) In the output process S23, the output unit 23 outputs the prediction result by the prediction unit 22.

[0040] Furthermore, the regression coefficients for each of the multiple target models are regression coefficients learned by a learning process including: a regression coefficient calculation process that calculates the regression coefficients for each of the multiple target models by referring to the learning data and an internal ratio parameter that defines the internal ratios of the multiple target models; a covariance calculation process that calculates covariance parameters of the prior distributions of the latent variables and a covariance matrix of the posterior distributions of the latent variables by referring to the learning data, the internal ratio parameter, the regression coefficients calculated by the regression coefficient calculation process, and information on the prior distributions of the latent variables; and an internal ratio parameter calculation process that calculates internal ratio parameters by referring to the learning data, the regression coefficients calculated by the regression coefficient calculation process, and the covariance matrix of the posterior distributions of the latent variables. As an example, the regression coefficients are regression coefficients calculated by the information processing method S1 according to this exemplary embodiment.

[0041] (Effect of information processing method S2) As described above, the information processing method S2 employs a configuration including an acquisition process S21 in which the acquisition unit 21 acquires inference data, a prediction process S22 in which the prediction unit 22 derives a plurality of prediction results by applying the regression coefficients for each of a plurality of target models to the inference data, and an output process S23 in which the output unit 23 outputs the prediction results by the prediction unit 22. The regression coefficients for each of the plurality of target models are regression coefficients learned by a learning process including: a regression coefficient calculation process that calculates a regression coefficient for each of the plurality of target models by referring to the learning data, the interior ratio parameters, the regression coefficients calculated by the regression coefficient calculation process, and information on the prior distribution of the hidden variables, and a covariance calculation process that calculates covariance parameters of the prior distribution of the hidden variables and a covariance matrix of the posterior distribution of the hidden variables; and an interior ratio parameter calculation process that calculates interior ratio parameters by referring to the learning data, the regression coefficients calculated by the regression coefficient calculation process, and the covariance matrix of the posterior distribution of the hidden variables. Therefore, according to the information processing method S2, the same effects as those of the information processing device 1 described above can be obtained.

[0042] 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.

[0043] 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).

[0044]

number

[0045] 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. k are 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.

[0046] 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.

[0047] 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.

[0048] 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:

[0049]

number

[0050] The following equations (3A) to (3E) are used:

[0051]

number

[0052] 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.

[0053] 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.

[0054] (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.

[0055] (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.

[0056] Examples of data stored in memory unit 15A include, but are not limited to, target data TD, internal division ratio parameters RP, regression coefficients RC, distribution information DI, learning results LR, inference data PD, and prediction results PR, as shown in FIG. 5.

[0057] 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.

[0058]

number

[0059] The internal ratio parameter RP is a parameter that defines the relative weights of multiple state space models in an LPV model, and is also called a scheduling parameter. The internal ratio parameter RP is also called a weight parameter RP or a scheduling parameter RP. As an example, the internal ratio parameter RP is given by the following equation (5) for each of m models (model 1 to model m).

[0060]

number

[0061] 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 specified in the above expression. This is because the interior ratio parameter RP is expressed as an interior 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 internal ratio parameter RP may be expressed as an internal ratio parameter vector or an internal ratio parameter matrix.

[0062] 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

[0063] The regression coefficient RC is a coefficient in the L2PV regression model. The regression coefficient RC is expressed as the following equation (6).

[0064]

number

[0065] 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.

[0066] Hidden variable z k The prior distribution p(z k ) is expressed as the following equation (7).

[0067]

number

[0068] 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.

[0069]

number

[0070] Hidden variable z k The posterior distribution p(z k |~y k ,~x k ,W,η) is expressed as the following equation (9).

[0071]

number

[0072] 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 .

[0073] 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).

[0074]

number

[0075] Therefore, the hidden variable z k Even when a normal distribution is used as the posterior distribution of , a suitable calculation can be performed.

[0076] 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 internal ratio parameter RP and the calculated regression coefficient RC.

[0077] 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.

[0078] The predicted result PR is a predicted result obtained by the L2PV regression model. Examples of the predicted result PR will be described later.

[0079] (Communication unit 16A) Communication unit 16A is an interface that transmits and receives data via a network. Examples of 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.

[0080] (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.

[0081] (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 also includes an acquisition unit 11, a regression coefficient calculation unit 12, a covariance calculation unit 13, an interior 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 interior ratio parameter calculation unit 14, the output unit 15, the initial value determination unit 16, the convergence determination unit 17, and the prediction unit 18 function as an acquisition means, a regression coefficient calculation means, a covariance calculation means, an interior ratio parameter calculation means, an output means, an initial value determination means, a convergence determination means, and a prediction means, respectively. Specific examples of the processing performed by each unit will be described later with reference to different drawings.

[0082] 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 hidden variables z k Another example of data acquired by the acquisition unit 11 is inference data PD. The acquisition unit 11 stores the acquired data in the storage unit 15A.

[0083] The regression coefficient calculation unit 12 calculates a regression coefficient RC for each of the plurality of target models by referencing an interior ratio parameter RP that defines the interior ratio of the plurality of target models and the target data TD. As one example, the interior ratio parameter RP referenced by the regression coefficient calculation unit 12 is the initial value of the interior ratio parameter RP determined by an initial value determination unit 16, which will be described later. As another example, the interior ratio parameter RP referenced by the regression coefficient calculation unit 12 is the interior ratio parameter RP calculated by an interior ratio parameter calculation unit 14, which will be described later. The regression coefficient calculation unit 12 stores the calculated regression coefficient RC in the memory unit 15A.

[0084] The covariance calculation unit 13 calculates the target data TD, the interior ratio parameter RP, the regression coefficient RC, and the hidden 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.

[0085] The internal ratio parameter calculation unit 14 calculates the target data TD, the regression coefficients RC, and the hidden variables z k The internal ratio parameter calculation unit 14 calculates the internal ratio parameter RP by referring to the covariance matrix Ψ of the posterior distribution of the internal ratio parameter RP. The internal ratio parameter calculation unit 14 stores the calculated internal ratio parameter RP in the storage unit 15A.

[0086] The output unit 15 outputs the regression coefficient RC and the interior ratio parameter RP (learning result LR) calculated by the interior ratio parameter calculation unit 14. As one example, the output unit 15 outputs an image including the regression coefficient RC and the interior ratio parameter RP calculated by the interior ratio parameter calculation unit 14 to the input / output unit 17A. As another example, the output unit 15 outputs the regression coefficient RC and the interior ratio parameter RP calculated by the interior ratio parameter calculation unit 14 when the convergence determination unit 17 described later determines that the calculation related to the interior ratio parameter RP has converged.

[0087] Initial value determination unit 16 determines the initial value of interior ratio parameter RP referenced by regression coefficient calculation unit 12. Initial value determination unit 16 stores the determined initial value of interior ratio parameter RP in storage unit 15A.

[0088] The convergence determination unit 17 determines whether or not the calculation related to the interior ratio parameter RP has converged. The convergence determination unit 17 supplies the determination result to the output unit 15.

[0089] The prediction unit 18 derives multiple prediction results PR by applying the regression coefficients RC for each of the multiple target models to the inference data PD. The prediction unit 18 stores the derived prediction results PR in the storage unit 15A. Therefore, the prediction unit 18 can derive multiple prediction results PR for each regression coefficient RC.

[0090] (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).

[0091]

number

[0092] 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).

[0093]

number

[0094] (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.

[0095] 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

[0096] 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. k The prior distribution p(z k ) may further be obtained.

[0097] (Step S16: Initial value determination process) Next, in step S16, the initial value determination unit 16 determines the initial value of the interior 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 interior ratio parameter RP as a random value. By the initial value determination unit 16 determining the initial value of the interior 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 interior ratio parameter RP have been explained above, so a detailed explanation will be omitted here.

[0098] (Step S12: Regression coefficient calculation process) Subsequently, in step S12, the regression coefficient calculation unit 12 calculates the internal ratio parameter (internal ratio parameter vector) RP expressed as the following equation (13):

[0099]

number

[0100] and the target data TD expressed as the following equation (14):

[0101]

number

[0102] and a regression coefficient RC for each of the plurality of target models, which is expressed as the following equation (15) with reference to

[0103]

number

[0104] As an example, the regression coefficient calculation unit 12 calculates a regression coefficient RC expressed as the following equation (17) by referring to the interior division ratio parameter RP and the target data TD using the following equation (16).

[0105]

number

[0106]

number

[0107] 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

[0108] (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):

[0109]

number

[0110] and the internal ratio parameter (internal ratio parameter vector) RP expressed as the following equation (19):

[0111]

number

[0112] and the regression coefficient RC expressed as the following equation (20):

[0113]

number

[0114] 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):

[0115]

number

[0116] (where Λ k is given by the following equation (22)

[0117]

number

[0118] 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):

[0119]

number

[0120] 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.

[0121] (Step S14: Internal division ratio parameter calculation process) Subsequently, in step S14, the internal division ratio parameter calculation unit 14 calculates the target data TD expressed as the following equation (24):

[0122]

number

[0123] and the regression coefficient RC expressed as the following equation (25):

[0124]

number

[0125] 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 internal division ratio parameter (internal division ratio parameter vector) RP expressed as the following equation (26) is obtained.

[0126]

number

[0127] As an example, the interior division ratio parameter calculation unit 14 calculates (updates) the following equation (27):

[0128]

number

[0129] By μ k The interior ratio parameter calculation unit 14 calculates (updates) the interior ratio parameter RP by executing the process of calculating the following equation (28) under a constraint condition (restriction condition) regarding the sum of the interior ratio parameters RP.

[0130]

number

[0131] By μ k By performing the process of calculating the internal ratio parameter (internal ratio parameter vector) RP expressed by the following equation (30) under the constraint condition (constraint condition) expressed by the following equation (29),

[0132]

number

[0133]

number

[0134] Here, the first equation of the above constraints 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 among the constraint conditions indicates that the sum of the interior ratio parameter RP across the model-related indexes is 1. Furthermore, the second equation among the constraint conditions indicates that the value of the interior ratio parameter RP is equal to or greater than 0. In this way, by calculating the interior ratio parameter RP under the constraint conditions, the interior ratio parameter calculation unit 14 can suitably calculate the interior ratio parameter RP even when, for example, a normal distribution is adopted as the posterior distribution of the latent variables.

[0135] (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 interior 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 equation (31),

[0136]

number

[0137] 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.

[0138] If the convergence determination unit 17 determines that "convergence has occurred," the process proceeds to the output process S15, and if it determines that "convergence has not occurred," the process returns to the regression coefficient calculation process S12, and the calculation of the regression coefficient RC is repeated.

[0139] (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 interior ratio parameter RP (learning result LR) calculated by the interior ratio parameter calculation unit 14 in step S14. In this way, if the convergence determination unit 17 determines that the calculation related to the interior 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.

[0140] 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.

[0141] 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.

[0142] 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 (32), in the regression coefficient calculation process in step S12.

[0143]

number

[0144] 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 interior 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.

[0145] (Example of processing executed by the prediction unit 18) The prediction unit 18 derives multiple prediction results PR by applying a regression coefficient RC for each of multiple target models to the inference data PD. As an example, if the inference data PD is the internal state quantity x0 at the 0th step, the prediction unit 18 derives multiple prediction results PR by applying the regression coefficient RC for each of the multiple target models to the internal state quantity x0.

[0146] For example, the prediction unit 18 calculates the regression coefficient W of the model 1. (1) The predicted result P1 is derived by applying the regression coefficient W of Model 2. (2)A prediction result P2 is derived by applying the above formula. In this case, an example of a graph that the output unit 15 displays via the input / output unit 17A is shown in Fig. 9. Fig. 9 shows another example of a graph that the output unit 15 displays via the input / output unit 17A.

[0147] In the example shown in FIG. 9, the output unit 15 uses the internal state quantity x0 as the inference data PD, and the prediction unit 18 calculates the regression coefficient W of the model 1. (1) The predicted result P1 and the regression coefficient W of Model 2 (2) and the prediction result P2 to which the above-mentioned method is applied 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, it is possible to generate a prediction result PR having a range for the inference data PD.

[0148] For example, consider a model that predicts the sales of drinking water in a store. As an example, assume that the target data TD used to train Model 1 indicates the sales when a measure is implemented, and the target data TD used to train Model 2 indicates the sales when the content volume is changed.

[0149] In this case, for example, the prediction unit 18 calculates the regression coefficient W of the model 1 at a certain date and time x0. (1) By applying the above, the prediction unit 18 derives a sales prediction result P1 when a measure is implemented at a certain date and time x0. In addition, the prediction unit 18 calculates the regression coefficient W (2) By applying the above formula, the predicted sales result P2 when the content capacity is changed at a certain date and time x0 is derived.

[0150] (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 target data TD and the internal ratio parameter RP that defines the internal ratio of the plurality of target models. Also, in the information processing device 1A, the target data TD, the internal ratio parameter RP, the regression coefficient RC, and the hidden variable z k With reference to the information about the prior distribution of the hidden variable z kThe covariance parameters of the prior distribution of and the hidden variable z k In the information processing device 1A, the target data TD, the regression coefficient RC, and the latent variable z k The information processing device 1A calculates the internal ratio parameter RP by referring to the covariance matrix of the posterior distribution of the regression coefficient RC. The information processing device 1A outputs the regression coefficient RC and the calculated internal ratio parameter RP.

[0151] With this configuration, the information processing device 1A calculates the internal ratio parameter RP as a hidden variable z k By treating it as (the posterior probability of) and applying a learning method for a hidden variable model in machine learning, it is possible to set a highly accurate internal ratio parameter RP even if the internal ratio parameter RP is unknown.

[0152] 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.

[0153] (Configuration of information processing device 2A) The configuration of the information processing device 2A will be described with reference to Fig. 10. Fig. 10 is a block diagram showing the configuration of the information processing device 2A. As shown in Fig. 10, the information processing device 2A includes a control unit 20A, a storage unit 25A, a communication unit 26A, and an input / output unit 27A.

[0154] 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, learning results LR, and prediction results PR, as shown in Fig. 10. The inference data PD, learning results LR, and prediction results PR are as described above.

[0155] The communication unit 26A is an interface that transmits and receives data via a network, similar to the above-described communication unit 16A.

[0156] 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.

[0157] (Control unit 20A) The control unit 20A controls each of the components 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.

[0158] The acquisition unit 21 acquires the inference data PD and stores the acquired inference data PD in the storage unit 25A.

[0159] The prediction unit 22 derives a plurality of prediction results PR by applying a regression coefficient for each of a plurality of target models to the inference data PD. The regression coefficients applied by the prediction unit 22 are included in the learning result LR. The prediction unit 22 stores the derived plurality of prediction results PR in the memory unit 25A.

[0160] The regression coefficients for each of the multiple target models applied by the prediction unit 22 are regression coefficients learned by a learning process including: a regression coefficient calculation process that calculates the regression coefficient for each of the multiple target models by referring to the learning data and internal ratio parameters that define the internal ratios of the multiple target models; a covariance calculation process that calculates 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 learning data, the internal ratio parameters, the regression coefficients calculated by the regression coefficient calculation process, and information on the prior distribution of the latent variables; and an internal ratio parameter calculation process that calculates internal ratio parameters by referring to the learning data, the regression coefficients calculated by the regression coefficient calculation process, and the covariance matrix of the posterior distribution of the latent variables. An example of the learning data is the target data TD described above.

[0161] 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 FIG. 9. That is, the output unit 23 displays graphs showing at least two prediction results PR out of the multiple prediction results PR, the graphs being defined by regression coefficients, in a manner that allows them to be distinguished from one another. Therefore, the output unit 23 can generate a prediction result PR having a range for the inference data PD (as an example, a prediction result having a range defined by the prediction result P1 and the prediction result P2 shown in FIG. 9).

[0162] (Configuration of information processing device 2A) As described above, in the information processing device 2A, the regression coefficients for each of the plurality of target models learned by the learning process are applied to the inference data PD to derive a plurality of prediction results PR.

[0163] With this configuration, the information processing device 2A treats the internal ratio parameters as latent variables (posterior probabilities) and applies a learning method for a latent variable model in machine learning, thereby making it possible to set highly accurate internal ratio parameters even if the internal ratio parameters are unknown.

[0164] Furthermore, since the prediction result PR is derived by applying the regression coefficients for each of the multiple target models used to set the highly accurate internal division ratio parameters to the inference data PD, it is possible to derive a prediction result PR with a range.

[0165] [Software implementation example] Some or all of the functions of the information processing devices 1, 1A, 2, 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.

[0166] 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 11. Figure 11 is a block diagram showing the hardware configuration of computer C that functions as each of the above devices.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [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.

[0172] (Appendix A1) an acquisition means for acquiring information about the target data, the number of models of a plurality of target models, and the prior distribution of the latent variables; a regression coefficient calculation means for calculating a regression coefficient for each of the plurality of target models by referring to the target data and an interior division ratio parameter that defines an interior division ratio of the plurality of target models; 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 interior ratio parameter, the regression coefficient, and information on the prior distribution of the latent variables; an internal ratio parameter calculation means for calculating the internal ratio parameter by referring to the target data, the regression coefficients, and a covariance matrix of the posterior distribution of the latent variables; an output means for outputting the regression coefficients and the interior ratio parameters calculated by the interior ratio parameter calculation means; An information processing device comprising:

[0173] (Appendix A2) The interior ratio parameter calculation means calculates the interior ratio parameters under constraints on the sum of the interior ratio parameters. 10. The information processing device according to claim 1,

[0174] (Appendix A3) The method further includes initial value determination means for determining an initial value of the internal division ratio parameter referenced by the regression coefficient calculation means. An information processing device according to appendix A1 or A2.

[0175] (Appendix A4) further comprising a convergence determination means for determining whether or not the calculation regarding the interior division ratio parameter has converged; The output means outputs the regression coefficients and the interior division ratio parameters when the convergence determination means determines that convergence has occurred. An information processing device according to any one of appendices A1 to A3.

[0176] (Appendix A5) 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. An information processing device according to any one of appendices A1 to A4.

[0177] (Appendix A6) The output means further outputs the covariance parameters of the prior distribution of the latent variables calculated by the covariance calculation means and the covariance matrix of the posterior distribution of the latent variables. An information processing device according to any one of appendices A1 to A5.

[0178] (Appendix A7) the acquisition means further acquires data for inference; The information processing device includes: a prediction means for deriving a plurality of prediction results by applying the regression coefficients for each of the plurality of target models to the inference data; It also has An information processing device according to any one of appendices A1 to A6.

[0179] (Appendix A8) The internal ratio parameters correspond to the expected values ​​of the hidden variables. An information processing device according to any one of appendices A1 to A7.

[0180] (Appendix A9) An acquisition means for acquiring data for inference; a prediction means for deriving a plurality of prediction results by applying a regression coefficient for each of a plurality of target models to the inference data; an output means for outputting a prediction result by the prediction means; Equipped with The regression coefficient for each of the plurality of target models is a regression coefficient calculation process for calculating a regression coefficient for each of the plurality of target models by referring to an internal division ratio parameter that defines an internal division ratio of the plurality of target models and learning data; a covariance calculation process that calculates 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 training data, the internal division ratio parameters, the regression coefficients calculated by the regression coefficient calculation process, and information on the prior distribution of the latent variables; an interior ratio parameter calculation process that calculates the interior ratio parameters by referring to the training data, the regression coefficients calculated by the regression coefficient calculation process, and a covariance matrix of the posterior distribution of the latent variables; The information processing device is configured to process the regression coefficients learned by a learning process including the following:

[0181] (Appendix A10) The output means displays graphs showing at least two of the plurality of prediction results, the graphs being defined by the regression coefficients, in a manner that allows them to be distinguished from one another. 10. The information processing device according to claim 9.

[0182] (Appendix A11) 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 covariance calculation means, the interior division ratio parameter calculation means, and the output means.

[0183] (Appendix A12) A program for causing a computer to function as the information processing device according to Appendix A9 or A10, the program causing a computer to function as the acquisition means, the prediction means, and the output means.

[0184] (Appendix A13) A non-transitory recording medium having recorded thereon a program for causing a computer to function as the information processing device according to any one of Appendix A1 to Appendix A10, the non-transitory recording medium having recorded thereon a program for causing a computer to function as the acquisition means, the regression coefficient calculation means, the covariance calculation means, the interior division ratio parameter calculation means, and the output means.

[0185] (Appendix A14) A non-transitory recording medium having recorded thereon a program for causing a computer to function as the information processing device described in Appendix A9 or A10, the non-transitory recording medium having recorded thereon a program for causing a computer to function as the acquisition means, the prediction means, and the output means.

[0186] [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.

[0187] (Appendix B1) At least one processor an acquisition process for acquiring information about the target data, the number of models of multiple target models, and the prior distribution of latent variables; a regression coefficient calculation process that calculates a regression coefficient for each of the plurality of target models by referring to an interior division ratio parameter that defines an interior division ratio of the plurality of target models and the target data; a covariance calculation process 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 interior ratio parameters, the regression coefficients, and information on the prior distribution of the latent variables; an interior ratio parameter calculation process that calculates the interior ratio parameters by referring to the target data, the regression coefficients, and a covariance matrix of the posterior distribution of the latent variables; an output process for outputting the regression coefficients and the interior ratio parameters calculated in the interior ratio parameter calculation process; An information processing method comprising:

[0188] (Appendix B2) In the interior ratio parameter calculation process, the at least one processor calculates the interior ratio parameters under a constraint on the sum of the interior ratio parameters. 1. The information processing method described in Appendix B1.

[0189] (Appendix B3) The at least one processor further includes an initial value determination process for determining an initial value of the interior division ratio parameter referenced by the regression coefficient calculation process. 1. An information processing method as described in Appendix B1 or B2.

[0190] (Appendix B4) The at least one processor further includes a convergence determination process for determining whether or not the calculation regarding the interior ratio parameter has converged; In the output process, when it is determined that the convergence determination process has converged, the at least one processor outputs the regression coefficients and the interior division ratio parameters. 1. An information processing method according to any one of appendices B1 to B3.

[0191] (Appendix B5) 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. 1. An information processing method according to any one of appendices B1 to B4.

[0192] (Appendix B6) In the output process, the at least one processor further outputs the covariance parameters of the prior distribution of the hidden variables calculated in the covariance calculation process and the covariance matrix of the posterior distribution of the hidden variables. 1. An information processing method according to any one of Appendices B1 to B5.

[0193] (Appendix B7) The at least one processor: In the acquisition process, data for inference is further acquired, a prediction process for deriving a plurality of prediction results by applying the regression coefficients for each of the plurality of target models to the inference data; It further includes 10. An information processing method according to any one of appendices B1 to B6.

[0194] (Appendix B8) The internal ratio parameters correspond to the expected values ​​of the hidden variables. 10. An information processing method according to any one of appendices B1 to B7.

[0195] (Appendix B9) At least one processor an acquisition process for acquiring data for inference; a prediction process for deriving a plurality of prediction results by applying regression coefficients for each of a plurality of target models to the inference data; an output process for outputting a prediction result obtained by the prediction process; Including, The regression coefficient for each of the plurality of target models is a regression coefficient calculation process for calculating a regression coefficient for each of the plurality of target models by referring to an internal division ratio parameter that defines an internal division ratio of the plurality of target models and learning data; a covariance calculation process that calculates 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 training data, the internal division ratio parameters, the regression coefficients calculated by the regression coefficient calculation process, and information on the prior distribution of the latent variables; an interior ratio parameter calculation process that calculates the interior ratio parameters by referring to the training data, the regression coefficients calculated by the regression coefficient calculation process, and a covariance matrix of the posterior distribution of the latent variables; The information processing method is a regression coefficient learned by a learning process including:

[0196] (Appendix B10) In the output process, the at least one processor displays graphs showing at least two of the plurality of prediction results, the graphs being defined by the regression coefficients, in a manner that allows them to be distinguished from one another. 1. An information processing method as described in Appendix B9.

[0197] [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.

[0198] (Appendix C1) at least one processor, an acquisition process for acquiring information about the target data, the number of models of multiple target models, and the prior distribution of latent variables; a regression coefficient calculation process that calculates a regression coefficient for each of the plurality of target models by referring to an interior division ratio parameter that defines an interior division ratio of the plurality of target models and the target data; a covariance calculation process 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 interior ratio parameters, the regression coefficients, and information on the prior distribution of the latent variables; an interior ratio parameter calculation process that calculates the interior ratio parameters by referring to the target data, the regression coefficients, and a covariance matrix of the posterior distribution of the latent variables; an output process for outputting the regression coefficients and the interior ratio parameters calculated by the interior ratio parameter calculation process; An information processing device that executes the above.

[0199] 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.

[0200] (Appendix C2) In the interior ratio parameter calculation process, the at least one processor calculates the interior ratio parameters under a constraint on the sum of the interior ratio parameters. 10. The information processing device according to claim 9, wherein the information processing device is a device for processing information according to claim 1.

[0201] (Appendix C3) the at least one processor: An initial value determination process is further performed to determine an initial value of the interior division ratio parameter referenced in the regression coefficient calculation process. An information processing device according to appendix C1 or C2.

[0202] (Appendix C4) the at least one processor further performs a convergence determination process to determine whether or not the calculation regarding the interior ratio parameter has converged; In the output process, when it is determined that the convergence determination process has converged, the at least one processor outputs the regression coefficients and the interior division ratio parameters. An information processing device according to any one of appendices C1 to C3.

[0203] (Appendix C5) 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. An information processing device according to any one of appendices C1 to C4.

[0204] (Appendix C6) In the output process, the at least one processor further outputs the covariance parameters of the prior distribution of the hidden variables calculated in the covariance calculation process and the covariance matrix of the posterior distribution of the hidden variables. An information processing device according to any one of appendices C1 to C5.

[0205] (Appendix C7) The at least one processor: In the acquisition process, data for inference is further acquired, a prediction process for deriving a plurality of prediction results by applying the regression coefficients for each of the plurality of target models to the inference data; Run the following again: An information processing device according to any one of appendices C1 to C6.

[0206] (Appendix C8) The internal ratio parameters correspond to the expected values ​​of the hidden variables. An information processing device according to any one of appendices C1 to C7.

[0207] (Appendix C9) at least one processor, an acquisition process for acquiring data for inference; a prediction process for deriving a plurality of prediction results by applying regression coefficients for each of a plurality of target models to the inference data; an output process for outputting a prediction result obtained by the prediction process; Run The regression coefficient for each of the plurality of target models is a regression coefficient calculation process for calculating a regression coefficient for each of the plurality of target models by referring to an internal division ratio parameter that defines an internal division ratio of the plurality of target models and learning data; a covariance calculation process that calculates 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 training data, the internal division ratio parameters, the regression coefficients calculated by the regression coefficient calculation process, and information on the prior distribution of the latent variables; an interior ratio parameter calculation process that calculates the interior ratio parameters by referring to the training data, the regression coefficients calculated by the regression coefficient calculation process, and a covariance matrix of the posterior distribution of the latent variables; The information processing device is configured to process the regression coefficients learned by a learning process including the following:

[0208] (Appendix C10) In the output process, the at least one processor displays graphs showing at least two of the plurality of prediction results, the graphs being defined by the regression coefficients, in a manner that allows them to be distinguished from one another. 10. The information processing device according to claim 9, [Explanation of symbols]

[0209] 1, 1A, 2, 2A Information processing equipment 11, 21 Acquisition Department 12 Regression coefficient calculation section 13 Covariance calculation section 14 Internal division parameter calculation section 15, 23 Output section 16 Initial value determination section 17 Convergence judgment section 18, 22 Prediction Section TD Target Data RP Internal Ratio Parameter RC regression coefficient DI distribution information LR learning results PD inference data PR prediction results

Claims

1. an acquisition means for acquiring information about the target data, the number of models of a plurality of target models, and the prior distribution of the latent variables; a regression coefficient calculation means for calculating a regression coefficient for each of the plurality of target models by referring to the target data and an interior division ratio parameter that defines an interior division ratio of the plurality of target models; 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 interior ratio parameter, the regression coefficient, and information on the prior distribution of the latent variables; an internal ratio parameter calculation means for calculating the internal ratio parameter by referring to the target data, the regression coefficients, and a covariance matrix of the posterior distribution of the latent variables; an output means for outputting the regression coefficients and the interior ratio parameters calculated by the interior ratio parameter calculation means; An information processing device comprising:

2. The interior ratio parameter calculation means calculates the interior ratio parameters under constraints on the sum of the interior ratio parameters. The information processing device according to claim 1 .

3. further comprising a convergence determination means for determining whether or not the calculation regarding the interior division ratio parameter has converged; The output means outputs the regression coefficients and the interior division ratio parameters when the convergence determination means determines that convergence has occurred.

3. The information processing device according to claim 1.

4. 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.

3. The information processing device according to claim 1.

5. the acquisition means further acquires data for inference; The information processing device includes: a prediction means for deriving a plurality of prediction results by applying the regression coefficients for each of the plurality of target models to the inference data; It also has 3. The information processing device according to claim 1.

6. The internal ratio parameters correspond to the expected values ​​of the hidden variables.

3. The information processing device according to claim 1.

7. An acquisition means for acquiring data for inference; a prediction means for deriving a plurality of prediction results by applying a regression coefficient for each of a plurality of target models to the inference data; an output means for outputting a prediction result by the prediction means; Equipped with The regression coefficient for each of the plurality of target models is a regression coefficient calculation process for calculating a regression coefficient for each of the plurality of target models by referring to an internal division ratio parameter that defines an internal division ratio of the plurality of target models and learning data; a covariance calculation process that calculates 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 training data, the internal division ratio parameters, the regression coefficients calculated by the regression coefficient calculation process, and information on the prior distribution of the latent variables; an interior ratio parameter calculation process that calculates the interior ratio parameters by referring to the training data, the regression coefficients calculated by the regression coefficient calculation process, and a covariance matrix of the posterior distribution of the latent variables; The information processing device is configured to process the regression coefficients learned by a learning process including the following:

8. The output means displays graphs showing at least two of the plurality of prediction results, the graphs being defined by the regression coefficients, in a manner that allows them to be distinguished from one another. The information processing device according to claim 7 .

9. At least one processor an acquisition process for acquiring information about the target data, the number of models of multiple target models, and the prior distribution of latent variables; a regression coefficient calculation process that calculates a regression coefficient for each of the plurality of target models by referring to an interior division ratio parameter that defines an interior division ratio of the plurality of target models and the target data; a covariance calculation process 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 interior ratio parameters, the regression coefficients, and information on the prior distribution of the latent variables; an interior ratio parameter calculation process that calculates the interior ratio parameters by referring to the target data, the regression coefficients, and a covariance matrix of the posterior distribution of the latent variables; an output process for outputting the regression coefficients and the interior ratio parameters calculated in the interior ratio parameter calculation process; 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 covariance calculation means, the interior ratio parameter calculation means, and the output means.

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