Algorithms for Bayesian Belief-Network Precomputation
Abstract
Bayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 1991
- Source ID
- 10.1055/s-0038-1634820
Entities
People
- E. H. Herskovits
- G. F. Cooper
Organizations
- Army Research Office
- National Science Foundation
- United States National Library of Medicine