Monte Carlo Methods and Applications

Catalog description

The Monte Carlo method uses random sampling to solve computational problems that would otherwise be intractable, and enables computers to model complex systems in nature that are otherwise too difficult to simulate. This course provides a first introduction to Monte Carlo methods from complementary theoretical and applied points of view, and will include implementation of practical algorithms. Topics include random number generation, sampling, Markov chains, Monte Carlo integration, stochastic processes, and applications in computational science. Students need a basic background in probability, multivariable calculus, and some coding experience in any language. Prerequisites: ( 21-266 Min. grade C or 21-254 Min. grade C or 21-269 Min. grade C or 21-259 Min. grade C or 21-256 Min. grade C or 21-268 Min. grade C) and ( 36-218 Min. grade C or 21-325 Min. grade C or 36-225 Min. grade C or 36-235 Min. grade C or 18-465 Min. grade C or 36-219 Min. grade C or 15-259 Min. grade C) Course Website: http://www.cs.cmu.edu/~kmcrane/random/