Monte Carlo: Basics
Statistical Mechanics
2007-05-23 v1
Abstract
An introduction to the basics of Monte Carlo is given. The topics covered include, sample space, events, probabilities, random variables, mean, variance, covariance, characteristic function, chebyshev inequality, law of large numbers, central limit theorem (stable distribution, Levy distribution), random numbers (generation and testing), random sampling techniques (inversion, rejection, sampling from a Gaussian, Metropolis sampling), analogue Monte Carlo and Importance sampling (exponential biasing, spanier technique).
Cite
@article{arxiv.cond-mat/0104215,
title = {Monte Carlo: Basics},
author = {K. P. N. Murthy},
journal= {arXiv preprint arXiv:cond-mat/0104215},
year = {2007}
}
Comments
74 pages; 16 figures