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HP Probabilistic Model Toolbox for MATLAB
Introduction
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.
PMT code can be
downloaded here:
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