Daniel Eaton and Kevin Murphy from the University of British Columbia have released a new Matlab/C/Java software package for Bayesian inference about fully observed Directed Acyclic Graph (DAG) structures using dynamic programming and MCMC.
I am pleased to announce the release of "BDAGL" (pronounced "be-daggle"), a Matlab/C package for learning Bayes net structures from fully observed data (discrete or continuous, static or time series). Its main novelty is that implements various algorithms for exact Bayesian inference of posterior features/ modes using dynamic programming. It also supports MCMC (on DAGs and orders) with various proposal distributions. Details are given at the URL below. Feedback is welcome.
You can download the software along with installation instructions, usage examples and references here.
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