General Purpose C++ Implementation for Inference and Learning in Bayesian and Markov Networks.
This code contains:
- an implementation for Bayesian and Markov Networks
- exact inference using Clique Tree Message Passing and Belief Propogation
- approximate inference using Gibbs Sampling and Loopy Belief Propagation over the Bethe Cluster Graph
- parameter learning for Bayesian Network from observed data samples (counting and smoothing)
- gradient ascent to learn parameters of a Markov Network from observed data samples
If you think you could use this code, let me know and I'll speed up the documentation!