Importance sampling: a review

Importance sampling: a review

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 samplin...

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Veröffentlicht in:Wiley interdisciplinary reviews. Computational statistics 2010-01, Vol.2 (1), p.54-60
Hauptverfasser: Tokdar, Surya T., Kass, Robert E.
Format: Artikel
Sprache:eng
Schlagworte:
Adaptation
Approximation
Data analysis
Data processing
Graphical methods
Importance sampling
Markov analysis
Markov chain sampling
Markov chains
Monte Carlo approximation
Monte Carlo simulation
Optimization
Population (statistical)
Resampling
Sampling
Sampling methods
Sampling techniques
Sequential sampling
Software
Statistical methods
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Beschreibung
Zusammenfassung: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
ISSN:1939-5108
1939-0068
DOI:10.1002/wics.56