Local Adaptive Importance Sampling for Multivariate Densities with Strong Nonlinear Relationships
适用于强非线性关系多元密度的局部自适应重要性抽样
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…