Software written in Matlab:

New! www.variational-bayes.org contains software for learning various models using variational approximations to Bayesian learning

For modelling independent identically distributed (iid) data:

• EM for Conditional Factor Analysis

• EM for Mixture of Factor Analyzers

• Variational Bayesian[*] Mixture of Factor Analysers

• Rectified Gaussian Belief Networks

For modelling time series data:

• EM for Hidden Markov Models for
  
  • real-valued Gaussian observations or
  • discrete-valued observations

• Variational Bayesian[*] Hidden Markov Models with discrete-valued observations: See Matt Beal's Software page

• EM for Linear Dynamical Systems (updated to include LDS with inputs)

• Variational Bayesian[*] State Space Models (aka Linear Dynamical Systems)

• Factorial Hidden Markov Models

Other software:

• DELVE: Data for Evaluating Learning in Valid Experiments -- is a collection of datasets, an environment within which this data can be used to assess the performance of learning methods, and a repository for the results of such assessments. This software and the documentation were written by the Delve Development Group.