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 e… Show more
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