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摘要翻译：
本文提出了一种自适应算法，迭代更新混合重要抽样密度的权值和分量参数，以优化用熵准则度量的重要抽样性能。结果表明，该方法适用于重要抽样密度的广泛类别，其中特别包括多元学生t分布的混合。在人工和实际的例子中研究了该方案的性能，特别强调了一种新的Rao-Blackwellisation设备的优点，该设备可以很容易地结合在更新方案中。
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英文标题：
《Adaptive Importance Sampling in General Mixture Classes》
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作者：
Olivier Capp\'e (LTCI), Randal Douc (CMAP), Arnaud Guillin (LATP),
Jean-Michel Marin (INRIA Futurs), Christian P. Robert (CEREMADE)
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最新提交年份：
2008
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分类信息：
一级分类：Statistics 统计学
二级分类：Computation 计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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英文摘要：
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 importance sampling performances, as measured by an entropy criterion. The method is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performances of the proposed scheme are 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.
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PDF链接：
https://arxiv.org/pdf/710.4242
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