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**). |