基于割平面的贝叶斯网络学习
Bayesian network learning with cutting planes
摘要：
研究了在有限的父集大小下，从完全离散数据中学习贝叶斯网络结构的问题。学习被明确地转换为优化问题，其中目标是找到使对数边际似然（BDe得分）最大化的BN结构。可编程逻辑器件编程，特别是SCIP框架，用于解决这个优化问题。在整数规划求解过程中，以割平面的形式加入了非循环约束。找到好的切割平面是该方法成功的关键-使用子IP来实现对这样的切割平面的搜索。结果表明，这是一个特别快的方法，精确BN学习。
英文摘要：
The problem of learning the structure of Bayesian networks from complete discrete data with a limit on parent set size is considered. Learning is cast explicitly as an optimisation problem where the goal is to find a BN structure which maximises log marginal likelihood (BDe score). Integer programming, specifically the SCIP framework, is used to solve this optimisation problem. Acyclicity constraints are added to the integer program (IP) during solving in the form of cutting planes. Finding good cutting planes is the key to the success of the approach -the search for such cutting planes is effected using a sub-IP. Results show that this is a particularly fast method for exact BN learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
