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Implicitly adaptive importance sampling

机译：隐含自适应重要性抽样

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摘要

Adaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling. Often the proposal distributions are standard probability distributions whose parameters are adapted based on the mismatch between the current proposal and a target distribution. In this work, we present an implicit adaptive importance sampling method that applies to complicated distributions which are not available in closed form. The method iteratively matches the moments of a set of Monte Carlo draws to weighted moments based on importance weights. We apply the method to Bayesian leave-one-out cross-validation and show that it performs better than many existing parametric adaptive importance sampling methods while being computationally inexpensive.
机译：自适应重要性采样是寻找重要性采样的良好建议分布的一类技术。通常，提案分布是标准概率分布，其参数基于当前提案和目标分布之间的不匹配来调整。在这项工作中，我们提出了一种隐含的自适应重要性采样方法，适用于复杂的分布，这些分布在封闭形式中不可用。该方法迭代地匹配一组蒙特卡罗的瞬间基于重要的重量绘制加权时刻。我们将该方法应用于贝叶斯休假的交叉验证，并表明它比许多现有的参数自适应重要性采样方法更好，同时计算得廉价。

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