Software for Bayesian inference with Mixed Graph Models

The following file provides Java classes and source code for our inference software, javaDMG. The javaDMG software does MCMC-based Bayesian inference in mixed graph models. This is an on-going project and we expect to expand it for several families of probabilistic models. The current version deals with Gaussian models only (a.k.a structural equation models). Download the file here:

• Source, documentation and compiled classes [Version 0.1, July 6th 2007]

Running javaDMG requires the Tetrad java library. You will need the latest JAR file containing the Tetrad library from the Tetrad project webpage. Go to the download directory and get the tetrad-4.3.8-6.jar file, or a more recent version.

The main reference for javaDMG is our UAI 2006 paper. The following are also relevant references to better understand mixed graph models:

• K. Bollen (1989). Structural Equations with Latent Variables. Wiley.
• T. Richardson and P. Spirtes (2002). Ancestral graph Markov models. Annals of Statistics. Technical report here.
• T. Richardson (2003). Markov properties for acyclic directed mixed graphs. Scandinavian Journal of Statistics. Technical report here.
• M. Drton and T. Richardson (2004) Iterative conditional fitting for Gaussian ancestral graph models. Proceedings of UAI'04.

Last modification: July 06th 2007