Bayesian networks (BNs) are one of the most commonly used models for representing uncertainty in medical diagnosis. Learning the exact structure of a BN is a challenging problem. This paper proposes a multi-threaded branch-and-bound (B&B) method, called parallel cycle-based branch-and-bound (parallel CB-B&B). On the one hand, CB-B&B improves the standard B&B method by leveraging two heuristics, namely the branching strategy and the bounding operators; on the other hand, the learning procedure is alleviated by executing CB-B&B over a set of parallel processors. In comparison with conventional exact structure learning approaches for BN, the obtained results demonstrate that the proposed CB-B&B is efficient. On average, it produces the exact structure for BN three times faster than the standard B&B version. We also present simulations on parallel CB-B&B which show a significant gain in terms of execution time.

中文翻译：

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贝叶斯网络学习中基于并行周期的分支定界方法

贝叶斯网络（BNs）是代表医学诊断不确定性的最常用模型之一。学习国阵的确切结构是一个具有挑战性的问题。本文提出了一种多线程分支定界（B＆B）方法，称为基于并行循环的分支定界（parallel CB-B＆B）。一方面，CB-B＆B利用分支策略和边界运算符这两种启发式方法改进了标准的B＆B方法。另一方面，通过在一组并行处理器上执行CB-B＆B可以减轻学习过程。与传统的BN精确结构学习方法相比，所获得的结果表明所提出的CB-B＆B是有效的。平均而言，它为BN生成准确的结构，是标准B＆B版本的三倍。