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摘要翻译：
提高重要采样器的效率是蒙特卡罗方法研究的中心。在马尔可夫链蒙特卡罗框架中，自适应方法通常是困难的，而重要性抽样中的对应方可以很容易地被证明和验证。提出了一种基于随机逼近的重要性采样器建议分布的迭代自适应学习方法。随机逼近法可以引入一般的迭代优化技术，如最小化-最大化算法。通过几个简单的算例说明了该方法在优化建议分布与目标之间的Kullback散度方面的有效性。
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英文标题：
《Stochastic adaptation of importance sampler》
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作者：
Heng Lian
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
Improving efficiency of importance sampler is at the center of research in Monte Carlo methods. While adaptive approach is usually difficult within the Markov Chain Monte Carlo framework, the counterpart in importance sampling can be justified and validated easily. We propose an iterative adaptation method for learning the proposal distribution of an importance sampler based on stochastic approximation. The stochastic approximation method can recruit general iterative optimization techniques like the minorization-maximization algorithm. The effectiveness of the approach in optimizing the Kullback divergence between the proposal distribution and the target is demonstrated using several simple examples.
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PDF链接：
https://arxiv.org/pdf/712.1342
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