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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.
Category: 它山之石 | Views: 457 | Added by: tes1991 | Tags: Software, Bayesian Network | Rating: 0.0/0
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