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.

翻译：本文针对贝叶斯网络（BN）的ε-接近参数调整问题：在给定条件概率表（中的若干行）中，寻找使贝叶斯网络上给定定量约束成立的概率条目的最小ε-接近修正。基于参数马尔可夫链的最新"区域验证"技术，我们提出了一种算法，其能力超越了现有任何技术。实验表明，对于包含多达8个参数的大型贝叶斯网络基准测试，ε-接近调整是可行的。特别地，通过允许（i）多个条件概率表中的参数变化和（ii）条件概率表间的参数依赖关系，我们处理了迄今为止鲜少关注的参数化贝叶斯网络子类。