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Software Packages for Graphical Models / Bayesian Networks
Software Packages for Graphical Models / Bayesian Networks
Written by Kevin Murphy. Last updated 31 October 2005.
Remarks
A much more detailed comparison of some of these software packages is available from Appendix B of Bayesian AI, by Ann Nicholson and Kevin Korb. This appendix is available here, and is based on the online comparison below.
An online French version of this page is also available (not necessarily up-to-date).
What do the headers in the table mean?
Src = source code included? (N=no) If so, what language?
API = application program interface included? (N means the program cannot be integrated into your code, i.e., it must be run as a standalone executable.)
Exec = Executable runs on W = Windows (95/98/NT), U = Unix, M = Mac, or - = any machine with a compiler.
Cts = are continuous (latent) nodes supported? G = (conditionally) Gaussians nodes supported analytically, Cs = continuous nodes supported by sampling, Cd = continuous nodes supported by discretization, Cx = continuous nodes supported by some unspecified method, D = only discrete nodes supported.
GUI = Graphical User Interface included?
Learns parameters?
Learns structure? CI = means uses conditional independency tests
Utility = utility and decision nodes (i.e., influence diagrams) supported?
Free? 0 = free (although possibly only for academic use). $ = commercial software (although most have free versions which are restricted in various ways, e.g., the model size is limited, or models cannot be saved, or there is no API.)
Undir? What kind of graphs are supported? U = only undirected graphs, D = only directed graphs, UD = both undirected and directed, CG = chain graphs (mixed directed/undirected).
Inference = which inference algorithm is used? jtree = junction tree, varelim = variable (bucket) elimination, MH = Metropols Hastings, G = Gibbs sampling, IS = importance sampling, sampling = some other Monte Carlo method, polytree = Pearl's algorithm restricted to a graph with no cycles, none = no inference supported (hence the program is only designed for structure learning from completely observed data)
Comments. If in "quotes", I am quoting the authors at their request.