Probabilistic Modeling
Toolkit for MATLAB
Overview
A set of MATLAB & MEX/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), and learning of model parameters from data.
PMT supports multiple inference methods, both exact and
approximate (e.g., winner takes all), based on the Bayesian
network equivalence of the model. Model parameters are
learned from data using maximum likelihood estimation (EM).
PMT also supports arbitrary distributions of training data,
something that comes useful in building recursive additive
mixtures of those models (e.g., boosting).
Download
pmt.tgz
Help
For any questions about PMT please contact
vladimir+pmt AT cs.rutgers.edu.