Probabilistic Model Toolkit for MATLAB The HP Probabilistic Model Toolbox (PMT) for MATLAB contains a set of MATLAB & C functions one can use to build basic static & dynamic probabilistic models. Current PMT provides support for the follow
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Probabilistic Model Toolkit for MATLAB Description

A set of MATLAB & C functions one can use to build basic static & dynamic probabilistic models The HP Probabilistic Model Toolbox (PMT) for MATLAB contains a set of MATLAB & C functions one can use to build basic static & dynamic probabilistic models. Current PMT provides support for the following probabilistic models: * Gaussian mixtures, * Factor analyzers, * Markov chains, * Hidden Markov models, and * Linear dynamic systems. For each probabilistic model, PMT provides functions for * Simulation (sampling from the model) * Inference (hidden state estimation) * Learning model parameters from data PMT supports multiple inference methods, both exact and approximate (e.g., winner takes all.) Model parameters are learned from data using maximum likelihood estimation (MLE). PMT also supports arbitrary distributions of training data.

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