Bucket Elimination：几种概率推理的统一框架
Bucket Elimination: A Unifying Framework for Several Probabilistic Inference
摘要：
概率推理算法找到最可能的解释，最大后验假设，最大期望效用和更新的信念被重新制定为一个消除型算法称为桶消除。这强调了在文献中出现的许多算法的共同原则，并阐明了它们与非串行动态规划算法的关系。我们还提出了一个一般的方式结合条件反射和消除在这个框架内。所有的算法作为一个功能的问题的结构的复杂性的界限。
英文摘要：
Probabilistic inference algorithms for finding the most probable explanation, the maximum aposteriori hypothesis, and the maximum expected utility and for updating belief are reformulated as an elimination--type algorithm called bucket elimination. This emphasizes the principle common to many of the algorithms appearing in that literature and clarifies their relationship to nonserial dynamic programming algorithms. We also present a general way of combining conditioning and elimination within this framework. Bounds on complexity are given for all the algorithms as a function of the problem's structure.
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。
