Adaptive Independent Metropolis-Hastings by Fast Estimation of Mixtures of Normals
Adaptive Independent Metropolis-Hastings by Fast Estimation of Mixtures of Normals
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DOI：
10.1198/jcgs.2009.07174
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发表时间：
2010-06-01
影响因子：
2.4
通讯作者：
Kohn, Robert
中科院分区：
文献类型：
--
作者：
Giordani, Paolo;Kohn, Robert
自适应大都会黑斯廷斯采样器使用从以前的抽签中获得的信息来自动重复地调整提案分布。适应需要仔细进行，以确保收敛到正确的目标分布，因为产生的链不是马尔可夫链。我们构建了一个自适应独立的大都会黑斯廷斯采样器，使用混合的法线作为建议分布。为了充分利用自适应采样的潜力，我们的算法经常更新法线的混合，从链的早期开始。该算法是建立在速度和可靠性，其采样性能进行评估与真实的和模拟的例子。我们的文章概述了自适应采样保持的条件。文章的在线补充给出了收敛性证明和高斯代码来实现算法。
Adaptive Metropolis Hastings samplers use information obtained from previous draws to tune the proposal distribution automatically and repeatedly. Adaptation needs to be done carefully to ensure convergence to the correct target distribution because the resulting chain is not Markovian. We construct an adaptive independent Metropolis-Hastings sampler that uses a mixture of normals as a proposal distribution. To take full advantage of the potential of adaptive sampling our algorithm updates the mixture of normals frequently, starting early in the chain. The algorithm is built for speed and reliability and its sampling performance is evaluated with real and simulated examples. Our article outlines conditions for adaptive sampling to hold. An online supplement to the article gives a proof of convergence and Gauss code to implement the algorithms.