Finding an $\epsilon$-close Variation of Parameters in Bayesian Networks

Author:

Bahare Salmani, Joost-Pieter Katoen

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI)

journal:

date:

2023-05-16 16:00:00

Abstract

This paper addresses the $\epsilon$-close parameter tuning problem for Bayesian Networks (BNs): find a minimal $\epsilon$-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 $\epsilon$-close tuning of large BN benchmarks with up to 8 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.

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