标题： 自适应独立Metropolis-Hastings通过快速估计混合正态分布 显示英文标题

标题： Adaptive Independent Metropolis-Hastings by Fast Estimation of Mixtures of Normals

摘要： 我们构建了一个自适应独立的Metropolis-Hastings抽样器，它使用正态混合分布作为建议分布。 为了充分利用自适应抽样的潜力，我们的算法从链的早期开始频繁更新正态混合分布。 该算法的设计注重速度和可靠性，并通过真实和模拟示例评估了其抽样性能。 我们的文章概述了自适应抽样的条件，并给出了一个易于理解的证明，即在这些条件下，抽样方案生成的迭代结果会收敛到目标分布。 显示英文摘要

摘要： 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 and gives a readily accessible proof that under these conditions the sampling scheme generates iterates that converge to the target distribution.