  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.
