Open AccessImportance sampling with transformed weights
Author(s) -
Vázquez M.A.,
Míguez J.
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2016.3462
Subject(s) - degeneracy (biology) , robustness (evolution) , monte carlo method , variance (accounting) , importance sampling , context (archaeology) , mathematics , sampling (signal processing) , monte carlo integration , population , algorithm , computer science , mathematical optimization , statistics , statistical physics , markov chain monte carlo , hybrid monte carlo , physics , telecommunications , business , chemistry , detector , sociology , biology , paleontology , biochemistry , accounting , bioinformatics , demography , gene
The importance sampling (IS) method lies at the core of many Monte Carlo‐based techniques. IS allows the approximation of a target probability distribution by drawing samples from a proposal (or importance ) distribution, different from the target, and computing importance weights (IWs) that account for the discrepancy between these two distributions. The main drawback of IS schemes is the degeneracy of the IWs, which significantly reduces the efficiency of the method. It has been recently proposed to use transformed IWs (TIWs) to alleviate the degeneracy problem in the context of population Monte Carlo, which is an iterative version of IS. However, the effectiveness of this technique for standard IS is yet to be investigated. The performance of IS when using TIWs is numerically assessed, and showed that the method can attain robustness to weight degeneracy thanks to a bias/variance trade‐off.