Introduction To Monte Carlo Algorithms
by Werner Krauth
Publisher: CNRS-Laboratoire de Physique Statistique 1998
Number of pages: 43
In these lectures, 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, "Faster than the clock algorithms".
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by Volker Springel - arXiv
These are lecture notes about high performance computing and numerical modelling in 43rd Saas Fee Advanced Course winter school, specifically covering the basics of numerically treating gravity and hydrodynamics in the context of galaxy evolution.
by Johan Hoffman, Claes Johnson
Computational foundation of thermodynamics based on deterministic finite precision computation without resort to statistics. A new 2nd Law without the concept of entropy is proved to be a consequence of the 1st Law and finite precision computation.
by K. P. N. Murthy - arXiv
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, etc.
by Rubin H Landau, Manuel J Paez, Cristian Bordeianu - Wiley-VCH
This text surveys many of the topics of modern computational physics from a computational science point of view. Its emphasis on learning by doing (assisted by many model programs), as with 2nd Edition, but with new materials as well as with Python.