Abstract:
Bayesian belief networks have grown to prominence because they provide compact representations of many domains, and there are algorithms to exploit this compactness. The next step is to allow compact representations of the conditional probability tables of a variable given its parents. In this paper we present such a representation in terms of parent contexts and provide an algorithm that exploits this compactness. The representation is in terms of rules that provide conditional probabilities in different contexts. The algorithm is based on eliminating the variables not needed in an answer in turn. The operations for eliminating a variable correspond to a form of partial evaluation, where we are careful to maintain the probabilistic dependencies necessary for correct probabilistic inference. We show how this new method can exploit more structure than previous methods for structured belief network inference.
Citations
|
4388
|
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
– Pearl
- 1988
|
|
904
|
Local computations with probabilities on graphical structures and their application to expert systems (with discussion
– Lauritzen, Spiegelhalter
- 1988
|
|
231
|
C.: Partial Evaluation in Logic Programming
– Lloyd, Shepherdson
- 1991
|
|
220
|
Bayesian updating in causal probabilistic networks by local computations
– Jensen, Lauritzen, et al.
- 1990
|
|
205
|
Probabilistic horn abduction and bayesian networks
– Poole
- 1993
|
|
187
|
Exploiting structure in policy construction
– Boutilier, Dearden, et al.
- 1995
|
|
183
|
Bucket elimination: A unifying framework for probabilistic inference
– Dechter
- 1999
|
|
176
|
Context-specific independence in bayesian networks
– Boutilier, Friedman, et al.
- 1996
|
|
86
|
Exploiting causal independence in bayesian network inference
– Zhang, Poole
- 1996
|
|
55
|
A sufficiently fast algorithm for finding close to optimal junction trees
– Becker, Geiger
- 1996
|
|
54
|
A new look at causal independence
– Heckerman, Breese
- 1994
|
|
45
|
Symbolic probabilistic inference in belief networks," Automated Reasoning
– Shachter, D'Ambrosio, et al.
- 1990
|
|
6
|
Exploiting contextual independence and approximation in belief network inference
– Poole
- 1997
|
|
5
|
Triangulation of graphs - algorithms giving small total state space
– Kj��rulff
- 1990
|
