利用领域知识筛选关键变量，加速近似MAP求解。
Speeding up approximate MAP by applying domain knowledge about relevant variables
- 基于领域知识识别相关变量并忽略无关变量。
- 在部分查询中比精确与近似MAP更快，结果仍较准确。
- 效果依赖于待求解的MAP变量数量，不具普适性。
贝叶斯网络中的最大后验概率（MAP）问题极其难以处理，即使采用近似方法也是如此。此前我们提出‘最省力解释’启发式方法，将中间变量（既非观测值也非MAP变量）分为相关变量（进行边缘化）和无关变量（采样赋值）。本研究探讨：针对特定查询所掌握的领域知识是否足以显著加速计算，从而超越精确与近似MAP，同时保持合理准确性。结果尚不明确，但表明该效果可能取决于具体查询，尤其是MAP变量的数量。
原文摘要 · Abstract (English)
The MAP problem in Bayesian networks is notoriously intractable, even when approximated. In an earlier paper we introduced the Most Frugal Explanation heuristic approach to solving MAP, by partitioning the set of intermediate variables (neither observed nor part of the MAP variables) into a set of relevant variables, which are marginalized out, and irrelevant variables, which will be assigned a sampled value from their domain. In this study we explore whether knowledge about which variables are relevant for a particular query (i.e., domain knowledge) speeds up computation sufficiently to beat both exact MAP as well as approximate MAP while giving reasonably accurate results. Our results are inconclusive, but also show that this probably depends on the specifics of the MAP query, most prominently the number of MAP variables.
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