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. The
key idea is to exploit scalable and powerful techniques for synthesis problems
in parametric Markov chains. Our techniques are applicable to arbitrarily many,
possibly dependent parameters that may occur in various CPTs. This lifts the
severe restrictions on parameters, e.g., by restricting the number of
parametrized CPTs to one or two, or by avoiding parameter dependencies between
several CPTs, in existing works for parametric Bayes networks (pBNs). We
describe how our techniques can be used for various pBN synthesis problems
studied in the literature such as computing sensitivity functions (and values),
simple and difference parameter tuning, ratio parameter tuning, and minimal
change tuning. Experiments on several benchmarks show that our prototypical
tool built on top of the probabilistic model checker Storm can handle several
hundreds of parameters.
