Fast Bayes and the dynamic junction forest
UNSPECIFIED (1999) Fast Bayes and the dynamic junction forest. ARTIFICIAL INTELLIGENCE, 107 (1). pp. 99-124. ISSN 0004-3702Full text not available from this repository.
It has been shown that junction tree algorithms can provide a quick and efficient method for propagating probabilities in complex multivariate problems when they can be described by a fixed conditional independence structure. In this paper we formalise and illustrate with two practical examples how these probabilistic propagation algorithms can be applied to high dimensional processes whose conditional independence structure, as well as their underlying distributions, are augmented through the passage of time. (C) 1999 Elsevier Science B.V. All rights reserved.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software|
|Journal or Publication Title:||ARTIFICIAL INTELLIGENCE|
|Publisher:||ELSEVIER SCIENCE BV|
|Number of Pages:||26|
|Page Range:||pp. 99-124|
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