Incremental compilation of Bayesian networks (original) (raw)
2003, Uncertainty in Artificial Intelligence
Most methods for exact probability propaga tion in Bayesian networks do not carry out the inference directly over the network, but over a secondary structure known as a junc tion tree or a join tree (JT). The process of obtaining a JT is usually termed compilation. As compilation is usually viewed as a whole process; each time the network is modified, a new compilation process has to be performed. The possibility of reusing an already exist ing JT in order to obtain the new one re garding only the modifications in the network has received only little attention in the liter ature. In this paper we present a method for incremental compilation of a Bayesian net work, following the classical scheme in which triangulation plays the key role. In order to perform incremental compilation we pro pose to recompile only those parts of the JT which may have been affected by the net work's modifications. To do so, we exploit the technique of maximal prime subgraph de composition in determining the minimal sub graph(s) that have to be recompiled, and thereby the minimal subtree(s) of the JT that should be replaced by new subtree(s). We fo cus on structural modifications: addition and deletion of links and variables.
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