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Finding an $ε$-close Variation of Parameters in Bayesian Networks
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.
Fine-Tuning the Odds in Bayesian Networks
This paper proposes various new analysis techniques for Bayes networks in which conditional probability tables (CPTs) may contain symbolic variables.
Bayesian Inference by Symbolic Model Checking
This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks.
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