Incremental compilation of Bayesian networks

M. Julia Flores, Jose A Gamez

Abstract:

Most methods for 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 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 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 may have been affected by the network's 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