Hidden Markov models, graphical models
Lecturer: Alex Simma
Date: Oct 8
Discrete State HMMs:
Continuous State HMMs:
Applications and Demonstrations:
- Kevin Murphy's Matlab toolboxes:
Hidden Markov models,
Kalman filters, and
Bayesian networks (directed graphical models).
- Recursive Bayesian Estimation Library (ReBEL):
Matlab code for linear/extended/unscented Kalman filters, and particle filters.
- R packages:
sspir (linear state space models, Kalman filters),
hmm.discnp (discrete-state HMMs with discrete observations),
HiddenMarkov (discrete-state HMMs with continuous observations), and
RHmm (discrete-state HMMs, but seems buggy and unstable).
- Hidden Markov Model Toolkit (HTK):
HMM code distributed as C libraries, focused on speech recognition.
- General Hidden Markov Model (GHMM) library:
Another HMM package written in C.
- BUGS: Monte Carlo methods for Bayesian inference in graphical models, including the WinBUGS graphical inteface.
C++ library for variational approximate inference in graphical models with discrete variables.
- Intel Probabilistic Network Library:
Eventually intended to support a wide range of graphical model types and inference algorithms, but the current beta release is more limited.