EPJ Web of Conferences
Volume 55, 2013SOS 2012 – IN2P3 School of Statistics
|Number of page(s)||21|
|Section||Multivariate Analysis Tools|
|Published online||01 July 2013|
Monte Carlo methods
Department of Statistics, Oxford University
Bayesian inference often requires integrating some function with respect to a posterior distribution. Monte Carlo methods are sampling algorithms that allow to compute these integrals numerically when they are not analytically tractable. We review here the basic principles and the most common Monte Carlo algorithms, among which rejection sampling, importance sampling and Monte Carlo Markov chain (MCMC) methods. We give intuition on the theoretical justification of the algorithms as well as practical advice, trying to relate both. We discuss the application of Monte Carlo in experimental physics, and point to landmarks in the literature for the curious reader.
© Owned by the authors, published by EDP Sciences, 2013
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 2.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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