This page is obsolete. Should use
Bayesian Belief Nets
These are links to external web sites.
As such, we have
no control over their content or availability. If you find any of the
following links unreachable (or the content is suspect), please let us
know and we will modify the list accordingly. Thank you.
Belief nets (aka Bayesian Networks, probability
causal nets) are models for representing uncertainty in our
Uncertainty arises in a variety of situations:
Belief Nets use probability theory to manage uncertainty by explicitly
representing the conditional dependencies between the different
components. This provides an intuitive graphical visualization of the
including the interactions among the various sources of uncertainty.
uncertainty in the experts themselves concerning their own
uncertainty inherent in the domain being modeled,
uncertainty in the knowledge engineer trying to translate the
and just plain
uncertainty as to the accuracy and actual availability of
information about Belief Nets:
- Judea Pearl, Probabilistic Reasoning in Intelligent Systems:
Networks of Plausible Inference, Morgan Kaufmann, 1988.
E. Neapolitan, Learning
Bayesian Networks, 2004.
Introduction to Bayesian Networks,
Springer Verlag, 1996
R.G. Cowell, A.P. Dawid, S.L. Lauritzen and D.J. Spiegelhalter,
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
from the popular press
for Belief Networks (inference, learning)
Return to Greiner's home page