Software Packages for Graphical Models
Written by Kevin Murphy.
Last updated 16 June 2014.
(Thanks to Alex Gorban for helping me with the switch to Google Sheets.)
of GM code at MLOSS
- Click here for a short article I wrote
for the ISBA (International Society for Bayesian Analysis) Newsletter,
December 2007, sumarizing some of the packages below.
for a more detailed discussion of some of these packages
written by Ann Nicholson and Kevin Korb in 2004.
for a French version of my comparison table (not necessarily up-to-date).
What do the headers in the table mean?
- Src = source code included? (N=no) If so, what language?
- 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)
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.)
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,
VMP = variational message passing,
EP = expectation propagation,
SL = the program is designed for
structure learning from completely observed data, not state estimation
If in "quotes", I am quoting the authors at their request.
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