Incremental Compilation of Bayesian networks

TL;DR

This paper introduces an incremental compilation method for Bayesian networks that efficiently updates the junction tree structure after network modifications by reusing existing components, reducing recomputation.

Contribution

It presents a novel approach using maximal prime subgraph decomposition to selectively recompile affected parts of the junction tree after structural changes.

Findings

Reduces recomputation time in Bayesian network updates

Efficiently updates junction trees following modifications

Improves scalability of Bayesian inference processes

Abstract

Most methods of exact probability propagation in Bayesian networks do not carry out the inference directly over the network, but over a secondary structure known as a junction tree or a join tree (JT). The process of obtaining a JT is usually termed {sl compilation}. As compilation is usually viewed as a whole process; each time the network is modified, a new compilation process has to be carried out. The possibility of reusing an already existing JT, in order to obtain the new one regarding only the modifications in the network has received only little attention in the literature. In this paper we present a method for incremental compilation of a Bayesian network, following the classical scheme in which triangulation plays the key role. In order to perform incremental compilation we propose to recompile only those parts of the JT which can have been affected by the networks modifications. To do so, we exploit the technique OF maximal prime subgraph decomposition in determining the minimal subgraph(s) that have to be recompiled, and thereby the minimal subtree(s) of the JT that should be replaced by new subtree(s).We focus on structural modifications : addition and deletion of links and variables.