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Bayesian Belief Nets
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Belief nets (aka Bayesian Networks, probability
nets,
causal nets) are models for representing uncertainty in our
knowledge.
Uncertainty arises in a variety of situations:

uncertainty in the experts themselves concerning their own
knowledge,

uncertainty inherent in the domain being modeled,

uncertainty in the knowledge engineer trying to translate the
knowledge,
and just plain

uncertainty as to the accuracy and actual availability of
knowledge.
Belief Nets use probability theory to manage uncertainty by explicitly
representing the conditional dependencies between the different
knowledge
components. This provides an intuitive graphical visualization of the
knowledge
including the interactions among the various sources of uncertainty.
General
information about Belief Nets:
Overview
textbooks:
 Judea Pearl, Probabilistic Reasoning in Intelligent Systems:
Networks of Plausible Inference, Morgan Kaufmann, 1988.
 Richard
E. Neapolitan, Learning
Bayesian Networks, 2004.

F. Jensen,
An
Introduction to Bayesian Networks,
Springer Verlag, 1996

R.G. Cowell, A.P. Dawid, S.L. Lauritzen and D.J. Spiegelhalter,
Probabilistic
Networks and Expert Systems, 1999.
 Stuart Russell and Peter Norvig, Artificial
Intelligence: A Modern Approach, Prentice Hall, 1995. (see esp
Ch 14,15, 19.)
 Daphne Koller and Nir Fridman,
Probabilistic Graphical Models
 Adnan Darwiche,
Modeling and Reasoning with Bayesian Networks
 Kevin Murphy,
Machine Learning: a Probabilistic Perspective
Presentations
Articles
from the popular press
Technical
Articles
Research
Groups
Software
for Belief Networks (inference, learning)
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