期望一致近似推理的推广与收敛性
Expectation Consistent Approximate Inference: Generalizations and Convergence
摘要：
循环置信传播的近似方法，包括期望传播和近似消息传递，在概率推理问题中引起了广泛的关注。本文提出并分析了Opper和Winther的期望一致近似推理方法的推广。所提出的方法，称为广义期望一致性（GEC），可以适用于最大后验概率（MAP）和最小均方误差（MMSE）估计。在这里，我们描述了它的不动点，收敛性和性能相对于副本预测的最优性。
英文摘要：
Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference problems. This paper proposes and analyzes a generalization of Opper and Winther's expectation consistent (EC) approximate inference method. The proposed method, called Generalized Expectation Consistency (GEC), can be applied to both maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimation. Here we characterize its fixed points, convergence, and performance relative to the replica prediction of optimality.
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