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".
Home page url
Download or read it online for free here:
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 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 Eric Ayars - California State University, Chico
Contents: Useful Introductory Python; Python Basics; Basic Numerical Tools; Numpy, Scipy, and MatPlotLib; Ordinary Differential Equations; Chaos; Monte Carlo Techniques; Stochastic Methods; Partial Differential Equations; Linux; Visual Python; etc.
by Richard Fitzpatrick
The purpose of the text is to demonstrate how computers can help deepen our understanding of physics and increase the range of calculations which we can perform. These lecture notes are writen for an undergraduate course on computational physics.