Local adaptive importance sampling for multivariate densities with strong nonlinear relationships

• 成果类型: Article
• 署名作者: Givens, GH; Raftery, AE
• 署名单位: University of Washington; University of Washington Seattle
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2291389
• 发表日期: 1996
• 页码: 132-141
• 关键词: monte-carlo integration; posterior distributions; Bayesian statistics; inference

摘要

We consider adaptive importance sampling techniques that use kernel density estimates at each iteration as importance sampling functions. These can provide more nearly constant importance weights and more precise estimates of quantities of interest than the sampling importance resampling algorithm when the initial importance sampling function is diffuse relative to the target. We propose a new method that adapts to the varying local structure of the target. When the target has unusual structure, such as strong nonlinear relationships between variables, this method provides estimates with smaller mean squared error than alternative methods.