Adaptive importance sampling in general mixture classes
Adaptive importance sampling in general mixture classes
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DOI：
10.1007/s11222-008-9059-x
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发表时间：
2008-12-01
影响因子：
2.2
通讯作者：
Robert, Christian P.
中科院分区：
文献类型：
--
作者：
Cappe, Olivier;Douc, Randal;Robert, Christian P.
在本文中，我们提出了一种自适应算法，该算法迭代更新混合重要性采样密度的权重和分量参数，以优化重要性采样的性能（通过熵准则衡量）。该方法称为 M-PMC，被证明适用于各种重要采样密度，其中特别包括多元 Student t 分布的混合。所提出方案的性能在人工和真实示例上进行了研究，特别强调了新颖的 Rao-Blackwellization 设备的优点，该设备可以轻松地合并到更新方案中。
In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the performance of importance sampling, as measured by an entropy criterion. The method, called M-PMC, is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performance of the proposed scheme is studied on both artificial and real examples, highlighting in particular the benefit of a novel Rao-Blackwellisation device which can be easily incorporated in the updating scheme.