Bayes Net Toolbox 2.0 for Matlab

[Kevin Murphy]

I am pleased to announce a major new release of the Bayes Net Toolbox,
a software package for Matlab 5 that supports inference and learning
in directed graphical models. Specifically, it supports exact and
approximate inference, discrete and continuous variables, static and
dynamic networks, and parameter and structure learning. Hence it can
handle a large number of popular statistical models, such as the
following:

PCA/factor analysis, logistic regression, hierarchical mixtures of
experts, QMR, DBNs, factorial HMMs, switching Kalman filters, etc.

For more details, and to download the software, please go to
   http://www.cs.berkeley.edu/~murphyk/Bayes/bnt.html

The new version (2.0) has been completely rewritten, making it much
easier to read, use and extend. It is also somewhat faster.  The main
change is that I now make extensive use of objects. (I used to use
structs, and a dispatch mechanism based on the type-tag
system in Abelson and Sussman.)  In addition, each inference
algorithm (junction tree, sampling, loopy belief propagation, etc.) is
now an object. This makes the code and documentation much more
modular. It also makes it easier to add special-case algorithms, and
to combine algorithms in novel ways (e.g., combining sampling and
exact inference).

I have gone to great lengths to make the source code readable, so it
should prove an invaluable teaching tool. In addition, I am hoping
that people will contribute algorithms to the toolbox, in the spirit
of the open source movement.

Kevin Murphy