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Importance sampling: a review
Author(s) -
Tokdar Surya T.,
Kass Robert E.
Publication year - 2009
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.56
Subject(s) - sampling (signal processing) , markov chain monte carlo , computer science , resampling , monte carlo method , importance sampling , markov chain , slice sampling , parametric statistics , gibbs sampling , adaptation (eye) , algorithm , machine learning , artificial intelligence , statistics , mathematics , bayesian probability , physics , optics , filter (signal processing) , computer vision
We provide a short overview of importance sampling—a popular sampling tool used for Monte Carlo computing. We discuss its mathematical foundation and properties that determine its accuracy in Monte Carlo approximations. We review the fundamental developments in designing efficient importance sampling (IS) for practical use. This includes parametric approximation with optimization‐based adaptation, sequential sampling with dynamic adaptation through resampling and population‐based approaches that make use of Markov chain sampling. Copyright © 2009 John Wiley & Sons, Inc. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Sampling