Finding an $ε$-close Variation of Parameters in Bayesian Networks
This addresses parameter tuning for Bayesian Networks, which is incremental as it builds on existing region verification techniques.
The paper tackles the problem of finding minimal parameter adjustments in Bayesian Networks to satisfy quantitative constraints, proposing an algorithm that enables feasible tuning for large benchmarks with up to 8 parameters.
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 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.