论文标题
边际搜索大约mmap
Approximate MMAP by Marginal Search
论文作者
论文摘要
我们在图形模型中提出了边际图（MMAP）查询的启发式策略。该算法基于将任务减少到多项式数量的边际推理计算。如果输入证据，计算要解释的变量的边际质量函数。边际信息增益用于决定要首先解释的变量，因此它们最可能的边际状态被转移到证据中。此过程的顺序迭代导致MMAP解释，并且在过程中获得的最小信息增益可以视为解释的置信度度量。初步实验表明，所提出的置信度度量正常检测到算法准确的实例，并且对于足够高的置信度水平，该算法给出了精确的溶液或与确切限制距离的锤击距离很小的近似解决方案。
We present a heuristic strategy for marginal MAP (MMAP) queries in graphical models. The algorithm is based on a reduction of the task to a polynomial number of marginal inference computations. Given an input evidence, the marginals mass functions of the variables to be explained are computed. Marginal information gain is used to decide the variables to be explained first, and their most probable marginal states are consequently moved to the evidence. The sequential iteration of this procedure leads to a MMAP explanation and the minimum information gain obtained during the process can be regarded as a confidence measure for the explanation. Preliminary experiments show that the proposed confidence measure is properly detecting instances for which the algorithm is accurate and, for sufficiently high confidence levels, the algorithm gives the exact solution or an approximation whose Hamming distance from the exact one is small.
