| Abstract
|
Bayesian belief networks show promise as a … Bayesian belief networks show promise as a representational framework for constructing expert systems; they provide platforms for knowledge acquisition and for normative probabilistic inference. Despite the intuitive appeal of this inference paradigm, the run-time complexity of general belief-network computation may be too great for solving many complex problems in a practical amount of time. Therefore, researchers have focused their attention on developing approximate or special-case algorithms for belief-network inference. For belief networks with a highly skewed distribution of joint probabilities, storing a small number of cases to capture a large proportion of the likely uses of the network may lead to a significant increase in the speed of inference. We report here preliminary results of a set of algorithms that cache (precompute and store) a small subset of a belief network to decrease the expected running time for probability computation. running time for probability computation.
|
| Author
|
Edward Herskovits +,
Gregory F. Cooper +
|
| Bibtype
|
techreport +
|
| Institution
|
Knowledge Systems, AI Laboratory +
|
| Key
|
KSL-89-35 +
|
| Modification dateThis property is a special property in this wiki.
|
1 May 2009 13:38:21 +
|
| Number
|
KSL-89-35 +
|
| Tag
|
Computer science +
|
| Title
|
Algorithms for Bayesian Belief-Network Precomputation +
|
| Tr id
|
KSL-89-35 +
|
| Year
|
1991 +
|
| Categories |
Technical Report,
Publication,
KSL Technical Report
|
