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.
| 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. |
| Elvira | Elvira consortium (Spain) | Java | Y | W,U,M | Cd,Cx | Y | Y | Y | Y | 0 | D | JTree,varelim,IS | "Also includes classification, abductive inference and model fusion" |
| 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 |
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