Finding an ϵ-Close Minimal Variation of Parameters in Bayesian Networks

Bahare Salmani, Joost-Pieter Katoen

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

Main Track. Pages 5720-5729.

Abstract

This paper addresses the ε-close parameter tuning problem for Bayesian

networks (BNs): find a minimal ε-close amendment of probability entries

in a given set of (rows in) conditional probability tables that make a

given quantitative constraint on the BN valid. Based on the

state-of-the-art “region verification” techniques for parametric Markov

chains, we propose an algorithm whose capabilities go

beyond any existing techniques. Our experiments show that ε-close tuning

of large BN benchmarks with up to eight parameters is feasible. In

particular, by allowing (i) varied parameters in multiple CPTs and (ii)

inter-CPT parameter dependencies, we treat subclasses of parametric BNs

that have received scant attention so far.