Importance Sampling Via the Estimated Sampler
Importance Sampling Via the Estimated Sampler
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DOI：
10.1093/biomet/asm076
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发表时间：
2007-12
期刊：
影响因子：
2.7
通讯作者：
Masayuki Henmi;Ryo Yoshida;S. Eguchi
中科院分区：
文献类型：
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作者：
Masayuki Henmi;Ryo Yoshida;S. Eguchi
讨论了数值积分的蒙特卡罗重要抽样方法。我们考虑一个参数抽样分布族，并建议使用由最大似然估计的抽样分布。所提出的方法的重要性抽样使用估计的抽样分布，以改善渐近方差的普通方法使用真实的抽样分布。这一论点与Henmi & Eguchi（2004）关于悖论的讨论密切相关。我们专注于一个条件下，所提出的方法获得的估计积分值具有渐近零方差。版权所有2007年，牛津大学出版社。
Monte Carlo importance sampling for evaluating numerical integration is discussed. We consider a parametric family of sampling distributions and propose the use of the sampling distribution estimated by maximum likelihood. The proposed method of importance sampling using the estimated sampling distribution is shown to improve the asymptotic variance of the ordinary method using the true sampling distribution. The argument is closely related to the discussion of the paradox in Henmi & Eguchi (2004). We focus on a condition under which the estimated integration value obtained by the proposed method has asymptotic zero variance. Copyright 2007, Oxford University Press.