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Inference in Bayesian Networks with Recursive Probability Trees: Data Structure Definition and Operations
Author(s) -
Cano Andrés,
GómezOlmedo Manuel,
Moral Serafín,
PérezAriza Cora B.,
Salmerón Antonio
Publication year - 2013
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21587
Subject(s) - inference , bayesian network , computer science , conditional probability , bayesian inference , variable elimination , graphical model , probabilistic logic , statistical inference , tree structure , approximate bayesian computation , tree (set theory) , artificial intelligence , machine learning , bayesian probability , data structure , theoretical computer science , mathematics , statistics , programming language , mathematical analysis
Recursive probability trees (RPTs) are a data structure for representing several types of potentials involved in probabilistic graphical models. The RPT structure improves the modeling capabilities of previous structures (like probability trees or conditional probability tables). These capabilities can be exploited to gain savings in memory space and/or computation time during inference. This paper describes the modeling capabilities of RPTs as well as how the basic operations required for making inference on Bayesian networks operate on them. The performance of the inference process with RPTs is examined with some experiments using the variable elimination algorithm.