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Monte Carlo methods
Author(s) -
Kroese Dirk P.,
Rubinstein Reuven Y.
Publication year - 2011
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.194
Subject(s) - markov chain monte carlo , monte carlo method , computer science , sampling (signal processing) , monte carlo method in statistical physics , hybrid monte carlo , monte carlo integration , rejection sampling , metropolis–hastings algorithm , algorithm , mathematical optimization , statistics , mathematics , filter (signal processing) , computer vision
Many quantitative problems in science, engineering, and economics are nowadays solved via statistical sampling on a computer. Such Monte Carlo methods can be used in three different ways: (1) to generate random objects and processes in order to observe their behavior, (2) to estimate numerical quantities by repeated sampling, and (3) to solve complicated optimization problems through randomized algorithms. WIREs Comp Stat 2012, 4:48–58. doi: 10.1002/wics.194 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Markov Chain Monte Carlo (MCMC)