644 results about "Prior probability" patented technology

A system for, and method of, text-to-phoneme (TTP) mapping and a digital signal processor (DSP) incorporating the system or the method. In one embodiment, the system includes: (1) a letter-to-phoneme (LTP) mapping generator configured to generate an LTP mapping by iteratively aligning a full training set with a set of correctly aligned entries based on statistics of phonemes and letters from the set of correctly aligned entries and redefining the full training set as a union of the set of correctly aligned entries and a set of incorrectly aligned entries created during the aligning and (2) a model trainer configured to update prior probabilities of LTP mappings generated by the LTP generator and evaluate whether the LTP mappings are suitable for training a decision-tree-based pronunciation model (DTPM).

Search results of a search query on a network are ranked according to an additional ranking function for the prior probability of relevance of a document based on document property. The document property may be the document's file type, the file size, the document language, or another query-independent property of the document. The query-independent values for each document property may be weighted according to relevance measurements of the document based on the document property. As more relevance measurements of the documents may be obtained, the query-independent values for each document property may be updated to reflect the new measurements.

A pose estimating device includes: a pose dictionary; an image feature extracting unit configured to extract observed image feature information; a past information storing unit configured to store past pose estimating information of the articulated object; a pose predicting unit configured to predict a present pose; a node predicting unit configured to calculate a prior probability as to whether each nodes includes a present pose; an identifying unit configured to calculate a likelihood of the observed image feature information for each node; a node probability calculating unit configured to calculate a probability in which the present pose belongs to the node in the upper layer; and a pose estimating unit configured to calculate pose information.

A tomographic reconstruction method and system incorporating Bayesian estimation techniques to inspect and classify regions of imaged objects, especially objects of the type typically found in linear, areal, or 3-dimensional arrays. The method and system requires a highly constrained model M that incorporates prior information about the object or objects to be imaged, a set of prior probabilities P(M) of possible instances of the object; a forward map that calculates the probability density P(D|M), and a set of projections D of the object. Using Bayesian estimation, the posterior probability p(M|D) is calculated and an estimated model MEST of the imaged object is generated. Classification of the imaged object into one of a plurality of classifications may be performed based on the estimated model MEST, the posterior probability p(M|D) or MAP function, or calculated expectation values of features of interest of the object.

A system for the representation, editing, evaluation, and inference of graphical models is disclosed which can be used to construct and evaluate a graphical model or graphical network and to calculate inference values. An efficient method of updating graphical models is demonstrated, and provides the basis for an improved system for manipulation and evaluation of probabilistic models. The graphical network editor is useful in the construction of graphical modes such as Bayesian Networks. The graphical network and network graphical user interface (GUI) are used in conjunction with each other to model a system wherein failure probabilities and the current state of components are taken into account to monitor the health and progress of a system for an engineer or engineering software to evaluate and monitor. The evaluation is useful in the monitoring of assets and other real systems having multiple, dependent, and independently operating components such as a pump, a manufacturing plant, a production line, an assembly line, where asset health and quality control is a concern. The asset components each influencing some overall outcome of a system or situation. Success or failure or probability of success, probability of failure and health of the system can be monitored and manipulated by altering the values of prior probability and posterior probability values. Failure correlation between components can be evaluated wherein failure rates of asset is unknown. Production and quality can be monitored and altered.

A method for tracking pedestrians in a video sequence, where each image frame of the video sequence corresponds to a time step, includes using marginal space learning to sample a prior probability distribution p(xt|Zt−1) of multi-person identity assignments given a set of feature measurements from all previous image frames, using marginal space learning to estimate an observation likelihood distribution p(zt|xt) of the set of features given a set of multi-person identity assignments sampled from the prior probability distribution, calculating a posterior probability distribution p(xt|Zt) from the observation likelihood distribution p(zt|xt) and the prior probability distribution p(xt|Zt−1), and using marginal space learning to estimate the prior probability distribution p(xt+1|Zt) for a next image frame given the posterior probability distribution p(xt|Zt) and a probability p(xt+1|xt), where the posterior probability distribution of multi-person identity assignments corresponds to a set of pedestrian detection hypotheses for the video sequence.

The invention relates to a method which automatically classifies motion targets learning online, models an image sequence background and detects the motion targets, scene variation, coverage viewing angle and partitioning scene, extracts and clusters characteristic vectors, and marks region classes; the number of the motion targets in a sub-region and certain threshold value initialize Gaussian distribution and prior probability to accomplish initialization of a classifier in accordance with the characteristic vectors of all the motion target regions that pass through the sub-region; the motion targets in the sub-region are classified and parameters of the classifier are online iterated and optimized; classification results in the process of tracking the motion targets are synthesized to output the classification result of the motion result that learns online. The invention is used for detection of abnormalities in monitor scenes, establishing rules for various class targets, enhancing security of monitor system, identifying objects in the monitor scenes, lessening complexity of identification algorithm, improving rate of identification, and for semantized comprehension for the monitor scenes, identifying classes of the motion target and aiding to comprehension for behavior events occurring in the scenes.

Disclosed herein is a method for identifying a member of a group with particular demographic characteristics. The method involves identifying purchasing behaviors of members of a group with particular demographic characteristics via market research, gathering transaction data from a financial account of a user at a financial institution for processing transactions of the user with the multiple of merchants, using the market research to set prior probabilities that a user is a member of the group in a Bayesian belief network, analyzing the transaction data of the user via a Bayesian belief network to determine whether the transaction data of the user indicates that the user is an individual member of the group with the particular demographic characteristics, and presenting to the user at least one of an offer and a savings opportunity from a merchant seeking to contact members of the group with the particular demographic characteristics.

A method of identifying at least one particular person of interest in a collection of digital images taken over time, includes providing the collection of digital images with each digital image containing one or more persons; storing in a digital database an appearance model having a set of features associated with the particular person of interest and a prior probability of the person of interest appearing in images by a particular photographer; and comparing the appearance model with features extracted from the digital images in the collection of digital images and using the prior probability and the appearance model to determine if the particular person of interest is in one or more digital images in the collection of digital images.

The invention is applicable to the field of biological engineering and provides a method for detecting mononucleotide polymorphism. The method comprises the following steps: sequencing fragments obtained by high throughput sequencing technology are compared on a referenced genome sequence; the likelihood ratio of various genotypes of the corresponding sites on the genome to be tested is obtained according to the sequencing mass fraction of each basic group in the genome to be tested and obtained by sequencing; the posterior probability of each genotype of each site on the referenced genome is calculated according to the likelihood ratio and the prior probability preset for each genotype, and the genotype which has the highest posterior probability is determined as the most likely right genotype of the corresponding sites on the genome to be tested to obtain the consistent sequence of the genome to be tested; and the sites of the genome to be tested, which are inconsistent with the sequence of the referenced genome in the consistent sequence are detected to obtain the polymorphism sites of the genome to be tested. The embodiment of the invention can achieve a more accurate test result as the influence of the prior probability on mononucleotide polymorphism test result is considered in advance.

A probabilistic digital signal processor using data from multiple instruments is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and / or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data from multiple instruments with the prior to generate a posterior probability distribution function passed (1) to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm or (2) iteratively to the dynamic state-space model. For example, the probabilistic processor operates on fused data using a physical model, where the data originates from a mechanical system or a medical meter or instrument, such as an electrocardiogram or pulse oximeter to generate new parameter information and / or enhanced parameter information.

A method and apparatus that provides an expert system for determining respiratory phase during ventilatory support of a subject. Discrete phase states are partitioned and prior probability functions and observed probability functions for each state are defined. The probability functions are based upon relative duration of each state as well as the flow characteristics of each state. These functions are combined to determine phase probabilities for each state using Bayes' theorem. The calculated probabilities for the states may then be compared to determine which state the subject is experiencing. A ventilator may then conform respiratory support in accordance with the most probable phase. To provide a learning feature, the probability functions may be adjusted during use to provide a more subject specific response that accounts for changing respiratory characteristics.

A method of segmenting tubular structures in digital images includes selecting a point in an image of a tubular object to be segmented, defining an initial state of the selected point, initializing measurement weights, a conditional probability distribution and a prior probability distribution of a feature space of the initial state, sampling the feature space from the prior probability distribution, estimating a posterior probability distribution by summing sample measurements weighted by the measurement weights, and segmenting a cross section of the tubular object from the posterior probability distribution.

A probabilistic digital signal processor using data from multiple instruments is described. In one example, a digital signal processor is integrated into a biomedical device. The processor is configured to: use a dynamic state-space model configured with a physiological model of a body system to provide a prior probability distribution function; receive sensor data input from at least two data sources; and iteratively use a probabilistic updater to integrate the sensor data as a fused data set and generate a posterior probability distribution function using all of: (1) the fused data set; (2) an application of Bayesian probability; and (3) the prior probability distribution function. The processor further generates an output of a biomedical state using the posterior probability function.

The invention relates to medical image processing and aims to provide a multiple organ segmentation method based on a deep convolutional neural network and a regional competition model. The multiple organ segmentation method based on the deep convolutional neural network and the regional competition model comprises processes of training a three-dimensional convolutional neural network; using the trained three-dimensional convolutional neural network to learn prior probability images of liver, spleen, kidney and background in CTA volume data; determining an initial segmentation region of each tissue according to the prior probability image of each tissue; determining the probability of each pixel point belonging to each of the four tissues in the image; establishing a multiple region segmentation model based on regional competition; solving the model using the convex optimization method; and performing post-processing to obtain the contour of each organ. The invention uses the convolutional neural network to automatically and rapidly detect positions of liver, spleen and kidney at the belly, thereby obtaining the prior probability image of each organ is obtained. Then the invention uses the regional competition model, so that the contours of liver, spleen and kidney can be accurately segmented.

The invention discloses a fault diagnosis method during the industrial process. The method comprises the steps of collecting historical normal data during the industrial process; calculating a detection statistics based on the historical normal data during the industrial process; collecting the to-be-detected data of the industrial process; on the condition that the industrial process is detected to be out of order, extracting a statistic feature based on the relative refactoring contribution method; according to the statistic feature, calculating a conditional probability density function in the fault mode and a conditional probability density function in the normal mode; according to the prior probability and the conditional probability density function, calculating a posterior probability; conducting the fault variable recognition on a current time sample based on the minimum risk Bayesian decision theory; according to a diagnosis result, updating the prior probability for the next time sample and conducting the fault diagnosis and recognition again for the next round. According to the technical scheme of the invention, the major failure variable, the secondary process variable and the normal variable of the current sample are distinguished. Meanwhile, the diagnosis result of the process variable of the previous time sample is applied to the diagnosis of the current sample. Therefore, the pollution effect during the fault diagnosis of the industrial process is eliminated.

The invention discloses an indoor positioning method and system and a positioning platform, and relates to the technical field of positioning of the mobile Internet. The method includes: obtaining prior probability of wireless signal strength detected at reference points according to the wireless signal strength of a positioned terminal and a signal strength probability distribution diagram of the reference points; obtaining posterior probability according to the prior probability and based on a Bayesian decision; and multiplying the posterior probability with a matrix of transition probability of the reference points, and outputting the reference point with the maximal transition probability as a positioning result of the positioned terminal. The method, system and platform build the indoor wireless signal strength probability distribution diagram through a transfer HMM model to realize a universal method for accurate positioning, and by combination of a wireless signal strength acquisition and preprocessing mechanism and a probability distribution matching method based on the transfer HMM model, an indoor accurate positioning service with good experience can be provided to a user.

The invention discloses a method for simultaneous localization and mapping of a mobile robot based on an improved particle filter. The method comprises the following steps: initializing an initial-moment pose of a robot; obtaining a t-moment prior probability density function according to the pose information at a t-1 moment, and generating a sampling particle set p; initializing the weights of particles; selecting an importance probability density function, generating a new sampling particle set q, calculating the weights of particles, updating the weights of the particles, and normalizing the weights; calculating the weighted sum of random sample particles at current moment t to express posterior probability density, and obtaining the moving pose and environmental map information; judging whether a new observed value is input; if so, returning; otherwise, ending the cycle; before returning, judging whether resampling is needed or not. According to the difference of the system state, a dynamic threshold is set for judgment, and a genetic algorithm is combined. According to the method disclosed by the invention, influence of a problem of particle degeneration on SLAM is reduced, and the calculated amount of the SLAM problem is reduced.

A probabilistic digital signal processor using data from multiple instruments is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and / or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data from multiple instruments with the prior to generate a posterior probability distribution function passed (1) to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm or (2) iteratively to the dynamic state-space model. For example, the probabilistic processor operates on fused data using a physical model, where the data originates from a mechanical system or a medical meter or instrument, such as an electrocardiogram or pulse oximeter to generate new parameter information and / or enhanced parameter information.

A tomographic reconstruction method and system incorporating Bayesian estimation techniques to inspect and classify regions of imaged objects, especially objects of the type typically found in linear, areal, or 3-dimensional arrays. The method and system requires a highly constrained model M that incorporates prior information about the object or objects to be imaged, a set of prior probabilities P(M) of possible instances of the object; a forward map that calculates the probability density P(D|M), and a set of projections D of the object. Using Bayesian estimation, the posterior probability p(M|D) is calculated and an estimated model MEST of the imaged object is generated. Classification of the imaged object into one of a plurality of classifications may be performed based on the estimated model MEST, the posterior probability p(M|D) or MAP function, or calculated expectation values of features of interest of the object.

Search results of a search query on a network are ranked according to an additional ranking function for the prior probability of relevance of a document based on document property. The ranking function can be adjusted based on a comparison of the language that a document is written in and the language that is associated with a search query. Both query-independent values and query-dependent values can be used to rank the document.

The invention provides a linear array SAR imaging method based on iterative minimization sparse Bayesian reconstitution. The method is prior distributional hypothesis based on a linear array SAR original echo signal measurement model, it is assumed that a prior probability density function of a scattering coefficient in a linear array SAR observation scene target space complies with complex exponential prior distribution, it is also assumed that a posterior probability density function of a linear array SAR original echo signal complies with Gaussian stochastic distribution, a reconstitution cost function of the scattering coefficient in the linear array SAR observation scene target space is set up through the Bayesian criterion, sparse reconstitution of the scattering coefficient in the linear array SAR observation scene target space is achieved through complex exponential distribution parametric optimization estimation and the iterative minimization constitution cost function, and therefore performance of linear array SAR sparse imaging is improved. The linear array SAR imaging method can be applied to the synthetic aperture radar imaging field, the global remote sensing field and other fields.

A biometric authentication system, authentication client terminal, and biometric authentication method are provided to reduce an expected value of the number of inputs of biometric data for authentication, while effectively preventing forgery. In a biometric authentication system, prior probabilities of enrolled users un and non-enrolled user u0 are previously set. 1:N matching is performed between feature data of a claimant v and matching feature data. The matching score is calculated for each enrolled user un. A ratio of the likelihood v=un to the likelihood v≠un is calculated for each enrolled user un using the calculated matching scores. Posterior probabilities of the enrolled users un and non-enrolled user u0 are calculated, using the likelihood ratios, and the prior probabilities of both the enrolled users un and the non-enrolled user u0. Then, determination is made by comparing each posterior probability with a first threshold.

According to an aspect of the invention, a method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of annotated images, each image including one or more candidate regions that have been identified as suspicious, deriving a set of descriptive feature vectors, where each candidate region is associated with a feature vector. A subset of the features are conditionally dependent, and the remaining features are conditionally independent. The conditionally independent features are used to train a naïve Bayes classifier that classifies the candidate regions as lesion or non-lesion. A joint probability distribution that models the conditionally dependent features, and a prior-odds probability ratio of a candidate region being associated with a lesion are determined from the training images. A new classifier is formed from the naïve Bayes classifier, the joint probability distribution, and the prior-odds probability ratio.

A probabilistic digital signal processor for medical function is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and / or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm. For example, the probabilistic processor operates using a physical model on data from a medical meter, where the medical meter uses a first physical parameter, such as blood oxygen saturation levels from a pulse oximeter, to generate a second physical parameter not output by the medical meter, such as a heart stroke volume, a cardiac output flow rate, and / or a blood pressure.

The invention provides a speaker marking method and system based on density peak value clustering and variation Bayes, and belongs to the field of voiceprint identification and mode identification. The method includes: firstly establishing a training voice database, and obtaining a general background model and a subspace model; then obtaining an i-vector factor of each fragment of to-be-detected voice data through an i-vector factor extraction method; and obtaining the number of speakers of the to-be-detected voice data and the prior probabilities of the speakers at each moment by employing a density peak value clustering algorithm, performing iterative estimation on the posterior probability of each speaker corresponding to each fragment by employing variational Bayes, and obtaining a speaker marking result. According to the method and system, problems of uncertainty of estimation of initial values of the number of the speakers and the prior probability of each speaker at each moment and large deviation generated by easy influence of the initial values on the speaker marking performance in the prior art are solved, and the accuracy, the stability and the flexibility of speaker marking are improved.

Systems, methods, and devices are provided for compressing a variety of signals, such as measured signals from a hydrocarbon operation, that may be stored and / or transmitted in compressed form. Segmentation tools and techniques are used to compress the signals. Segmentation techniques include breaking a signal into segments and representing the data samples of the signal as segment boundary points, which may reflect where changes occur in the signal, and segment parameters, which may be utilized to model the segmented data. Embodiments can be used in real-time or in batch modes. New data samples can influence previous segment boundary points and / or segment parameters in some cases. Systems may modify what has already been stored or displayed as a result in revising segmentation information based on analysis utilizing the new data samples. Embodiments may utilize different Bayesian analysis techniques including the use of prior probability distributions and maximum a posteriori analyses.

The invention discloses an intelligent inquisition method, an intelligent inquisition system, computer equipment and a storage medium. The intelligent inquisition method includes the following steps that: disease prior probability prediction is performed according to the main complaint content of a target object, disease prior probabilities for the target object are obtained; at least one candidate disease is determined based on the order of the disease prior probabilities; and at least one agent strategy correspondingly associated with the at least one candidate disease is determined from a pre-established multi-agent model based on reinforcement learning, wherein each agent strategy is used to indicate questions which are required to be raised for the output of next interaction under a current symptomatic state; based on each agent strategy, interactive consultation is conducted according to the interactive selection of the questions which are required to be raised for the output ofthe next interaction for the target object. With the intelligent inquisition method, the intelligent inquisition system, the computer equipment and the storage medium of the invention adopted, the rationality of inquisition logic for related diseases and user experience can be optimized with the accuracy of inquisition results ensured.

The invention discloses a robust speech characteristic extraction method based on sparse decomposition and reconfiguration, relating to a robust speech characteristic extraction method with sparse decomposition and reconfiguration. The robust speech characteristic extraction method solves the problems that 1, the selection of an atomic dictionary has higher the time complexity and is difficult tomeet the sparsity after signal projection; 2, the sparse decomposition of signals has less consideration for time relativity of speech signals and noise signals; and 3, the signal reconfiguration ignores the prior probability of atoms and mutual transformation of all the atoms. The robust speech characteristic extraction method comprises the following detailed steps of: step 1, preprocessing; step 2, conducting discrete Fourier transform and solving a power spectrum; step 3, training and storing the atom dictionary; step 4, conducting sparse decomposition; step 5, reconfiguring the speech spectrum; step 6, adding a Mel triangular filter and taking the logarithm; and step 7, obtaining sparse splicing of Mel cepstrum coefficients and a Mel cepstrum to form the robust characteristic. The robust speech characteristic extraction method is used for the fields of multimedia information processing.

The invention discloses a method and a device for realizing an association rule mining algorithm supporting a distributed computation. An HDFS (Hadoop Distributed File System) programming model is used to carry out two-stage analysis of a map function stage and a reduce function stage on the association rule mining algorithm, and the analysis steps comprises the following steps: step 1, a job scheduler is configured; step 2, a data set is read by a prior probability mapping module, and the data of the data set are converted by a map function into a value pair; step 3, the value pair processed in the step 2 is read by the prior probability reduction module, an ordering rule Top N containing an i item set is randomly generated by a reduce function, and the prior probability distribution value of a confidence coefficient is calculated at the same time; step 4, the same data set is read by a rule mapping module, and the data row of the data set is converted by the map function into the value pair; and step 5, the value pair processed in the step 4 and the prior probability distribution value in the step 3 are read by a rule reduction module, and the predication accuracy value of the ordering rule Top N is calculated by the reduce function. The method and the device for realizing the association rule mining algorithm supporting the distributed computation are mainly applied to the PA (Pridictive Apriori)-distribution type computing technology.