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Software Packages for Graphical Models / Bayesian Networks

来源： 作者：unkonwn 时间：2004-12-08 点击：

  • 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.
Name Authors Src API Exec Cts GUI Params Struct Utility Free Undir Inference Comments
AgenaRisk Agena N Y W,U Cx Y Y N N $ D JTree Simulation by Dynamic discretisation
Analytica Lumina N Y W,M G Y N N Y $ D sampling spread sheet compatible
Banjo Hartemink Java Y W,U,M Cd N N Y N 0 D none structure learning of static or dynamic networks of discrete variables
Bassist U. Helsinki C++ Y U G N Y N N 0 D MH Generates C++ for MCMC.
Bayda U. Helsinki Java Y WUM G Y Y N N 0 D ? Bayesian Naive Bayes classifier.
BayesBuilder Nijman (U. Nijmegen) N N W D Y N N N 0 D ? -
BayesiaLab Bayesia Ltd N N - Cd Y Y Y N $ CG jtree,G Structural learning, adaptive questionnaires, dynamic models
Bayesware Discoverer Bayesware N N WUM Cd Y Y Y N $ D ? Uses bound and collapse for learning with missing data.
B-course U. Helsinki N N WUM Cd Y Y Y N 0 D ? Runs on their server: view results using a web browser.
Belief net power constructor Cheng (U.Alberta) N W W D Y Y CI N 0 D ? -
BNT Murphy (U.C.Berkeley) Matlab/C Y WUM G N Y Y Y 0 D,U Many Also handles dynamic models, like HMMs and Kalman filters.
BNJ Hsu (Kansas) Java - - D Y N Y N 0 D jtree, IS -
BucketElim Rish (U.C.Irvine) C++ Y WU D N N N N 0 D Varelim -
BUGS MRC/Imperial College N N WU Cs W Y N N 0 D Gibbs -
Business Navigator 5 Data Digest Corp N N W Cd Y Y Y N $ D Jtree -
CABeN Cousins et al. (Wash. U.) C Y WU D N N N N 0 D 5 Sampling methods -
Causal discoverer Vanderbilt N N W - - N Y N 0 D - structure learning only
CoCo+Xlisp Badsberg (U. Aalborg) C/lisp Y U D Y Y CI N 0 U Jtree Designed for contingency tables.
CIspace Poole et al. (UBC) Java N WU D Y N N N 0 D Varelim -
DBNbox Roberts et al Matlab - - Y N Y N N Y D Various DBNs
Deal Bottcher et al R - - G Y Y Y N 0 D None Structure learning.
DeriveIt DeriveIt LLC N - - ? ? Y Y ? $ D Jtree Exploits local structure in CPDs.
Ergo Noetic systems N Y W,M D Y N N N $ D jtree -
GDAGsim Wilkinson (U. Newcastle) C Y WUM G N N N N 0 D Exact Bayesian analysis of large linear Gaussian directed models.
Genie U. Pittsburgh N WU WU D W N N Y 0 D Jtree -
GMRFsim Rue (U. Trondheim) C Y WUM G N N N N 0 U MCMC Bayesian analysis of large linear Gaussian undirected models.
GMTk Bilmes (UW), Zweig (IBM) N Y U D N Y Y N 0 D Jtree Designed for speech recognition.
gR Lauritzen et al. R - - - - - - - 0 - - Currently vaporware
Grappa Green (Bristol) R - - D N N) N N 0 D Jtree -
Hugin Expert Hugin N Y W G W Y CI Y $ CG Jtree -
Hydra Warnes (U.Wash.) Java - - Cs Y Y N N 0 U,D MCMC -
Ideal Rockwell Lisp Y WUM D Y N N Y 0 D Jtree GUI requires Allegro Lisp.
Java Bayes Cozman (CMU) Java Y WUM D Y N N Y 0 D Varelim, jtree -
KBaseAI Codeas N Y W,U D N N N N $ D varelim client/server architecture, multiple users, access control, query language
LibB Friedman (Hebrew U) N Y W D N Y Y N 0 D none Structure learning
MIM HyperGraph Software N N W G Y Y Y N $ CG Jtree Up to 52 variables.
MSBNx Microsoft N Y W D W N N Y 0 D Jtree -
Netica Norsys N WUM W G W Y N Y $ D jtree -
Optimal Reinsertion Moore, Wong (CMU) N N W,U D N Y Y N 0 D none structure learning
PMT Pavlovic (BU) Matlab/C - - D N Y N N 0 D special purpose -
PNL Eruhimov (Intel) C++ - - D N Y Y N 0 U,D Jtree A C++ version of BNT; will be released 12/03.
Pulcinella IRIDIA Lisp Y WUM D Y N N N 0 D ? Uses valuation systems for non-probabilistic calculi.
RISO Dodier (U.Colorado) Java Y WUM G Y N N N 0 D Polytree Distributed implementation.
Sam Iam Darwiche (UCLA) N N ? WU ? (Java executable) G ? Y Y N ? Y 0 D Recursive conditioning Also does sensitivity Analysis
Tetrad CMU N N WU G N Y CI N 0 U,D None -
UnBBayes ? Java - - D Y N Y N 0 D jtree K2 for struct learning
Vibes Winn & Bishop (U. Cambridge) Java Y WU Cx Y Y N N 0 D Variational Not yet available.
Web Weaver Xiang (U.Regina) Java Y WUM D Y N N Y 0 D ? -
WinMine Microsoft N N W Cx Y Y Y N 0 U,D None Learns BN or dependency net structure.
XBAIES 2.0 Cowell (City U.) N N W G Y Y N Y 0 CG Jtree -
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