Bayesian networks

[David Heckerman]

A Bayesian network (a.k.a. belief network) is a graphical, modular
representation of a joint probability distribution over a set of
variables.  A Bayesian network is often easy to build directly from
domain or expert knowledge and also can be learned from data, making
it an excellent representation language in which to combine domain
knowledge and data.

The march issue of CACM contains a tutorial on the representation as
well as three articles on applications.  Also, I've written a tutorial
on learning Bayesian networks containing many pointers to the
literature.  The tutorial (in part) will appear in the forthcoming
collection "Advances in Knowledge Discovery and Data Mining" edited by
U. Fayyad, G. Piatesky-Shapiro, P. Smyth, and R. Uthurusamy.  It can
be obtained via anonymous ftp at

research.microsoft.com://pub/tech-reports/winter94-95/tr-95-06.ps

or via my home page

http://www.research.microsoft.com/research/dtg/heckerma/heckerma.html.

David