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Acceleration of the Multiple-Try Metropolis algorithm using antithetic and stratified sampling

机译:使用对立和分层采样加速多次尝试Metropolis算法

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The Multiple-Try Metropolis is a recent extension of the Metropolis algorithm in which the next state of the chain is selected among a set of proposals. We propose a modification of the Multiple-Try Metropolis algorithm which allows for the use of correlated proposals, particularly antithetic and stratified proposals. The method is particularly useful for random walk Metropolis in high dimensional spaces and can be used easily when the proposal distribution is Gaussian. We explore the use of quasi Monte Carlo (QMC) methods to generate highly stratified samples. A series of examples is presented to evaluate the potential of the method.
机译:多次尝试Metropolis是Metropolis算法的最新扩展,其中从一组建议中选择了链的下一个状态。我们建议对“多次尝试都会”算法进行修改,以允许使用相关提案,尤其是对立提案和分层提案。该方法对于高维空间中的随机行走都会区特别有用,并且在提案分布为高斯分布时可以轻松使用。我们探索使用准蒙特卡洛(QMC)方法生成高度分层的样本。给出了一系列示例,以评估该方法的潜力。

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