Optimality in noisy importance sampling

Many applications in signal processing and machine learning require the study of probability density functions (pdfs) that can only be accessed through noisy evaluations. In this work, we analyze importance sampling (IS), i.e., IS working with evaluations target density. We present general framework derive optimal proposal densities for estimators. The proposals incorporate information variance realizations, proposing points regions where noise power is higher. also compare use previous optimality approaches considered a framework.