Introduction to Monte Carlo Methods
Abstract
Monte Carlo methods play an important role in scientific computation, especially when problems have a vast phase space. In this lecture an introduction to the Monte Carlo method is given. Concepts such as Markov chains, detailed balance, critical slowing down, and ergodicity, as well as the Metropolis algorithm are explained. The Monte Carlo method is illustrated by numerically studying the critical behavior of the twodimensional Ising ferromagnet using finitesize scaling methods. In addition, advanced Monte Carlo methods are described (e.g., the Wolff cluster algorithm and parallel tempering Monte Carlo) and illustrated with nontrivial models from the physics of glassy systems. Finally, we outline an approach to study rare events using a Monte Carlo sampling with a guiding function.
 Publication:

arXiv eprints
 Pub Date:
 May 2009
 arXiv:
 arXiv:0905.1629
 Bibcode:
 2009arXiv0905.1629K
 Keywords:

 Condensed Matter  Statistical Mechanics;
 Physics  Computational Physics
 EPrint:
 lecture at the third international summer school "Modern Computation Science", 15  26 August 2011, Oldenburg (Germany), see http://www.mcs.unioldenburg.de