Monte Carlo: Basics
by K. P. N. Murthy
Publisher: arXiv 2001
Number of pages: 76
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).
Home page url
Download or read it online for free here:
by Morten Hjorth-Jensen - University of Oslo
These notes should train you in an algorithmic approach to problems in the sciences, represented here by the unity of three disciplines, physics, mathematics and informatics. This trinity outlines the emerging field of computational physics.
by Jeffrey R. Chasnov - Harvey Mudd College
This course consists of both numerical methods and computational physics. MATLAB is used to solve various computational math problems. The course is primarily for Math majors and supposes no previous knowledge of numerical analysis or methods.
by Badis Ydri - arXiv
We give an elementary introduction to computational physics. We deal with the problem of how to set up working Monte Carlo simulations of matrix field theories which involve finite dimensional matrix regularizations of noncommutative field theories.
by Werner Krauth - CNRS-Laboratoire de Physique Statistique
The author discusses the fundamental principles of thermodynamic and dynamic Monte Carlo methods in a simple light-weight fashion. The keywords are Markov chains, Sampling, Detailed Balance, A Priori Probabilities, Rejections, Ergodicity, etc.