MAP Estimation for Graphical Models by Likelihood Maximization
MAP Estimation for Graphical Models by Likelihood Maximization
复制标题
通过似然最大化对图形模型进行 MAP 估计
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
S. Zilberstein
中科院分区：
文献类型：
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作者：
Akshat Kumar;S. Zilberstein
图模型中最大后验概率分配的计算是许多实际应用中的一个重要推理问题。几个可证明收敛的方法已经成功地开发使用线性规划（LP）松弛的MAP问题。我们提出了一种替代方法，它将MAP问题转化为简单贝叶斯网的混合推理。然后，我们推导出期望最大化（EM）算法，这种混合物也单调增加的MAP分配的下限，直到收敛。EM算法的更新方程在概念上和计算上都非常简单，并且可以使用类似于最大乘积计算的基于图的消息传递范例来实现。在真实蛋白质设计数据集上的实验表明，EM的收敛速度明显高于以前的LP松弛的方法MPLP。EM在大多数情况下还可以实现95%的最佳解决方案质量。
Computing a maximum a posteriori (MAP) assignment in graphical models is a crucial inference problem for many practical applications. Several provably convergent approaches have been successfully developed using linear programming (LP) relaxation of the MAP problem. We present an alternative approach, which transforms the MAP problem into that of inference in a mixture of simple Bayes nets. We then derive the Expectation Maximization (EM) algorithm for this mixture that also monotonically increases a lower bound on the MAP assignment until convergence. The update equations for the EM algorithm are remarkably simple, both conceptually and computationally, and can be implemented using a graph-based message passing paradigm similar to max-product computation. Experiments on the real-world protein design dataset show that EM's convergence rate is significantly higher than the previous LP relaxation based approach MPLP. EM also achieves a solution quality within 95% of optimal for most instances.
影响因子：
32.8
作者：
Wainwright, Martin J.;Jordan, Michael I.
通讯作者：
Jordan, Michael I.