The spring semester 2021 will generally take place online. New presence elements as of April 26 will be communicated by the lecturers.

# 401-3612-00L  Stochastic Simulation

 Semester Autumn Semester 2018 Lecturers F. Sigrist Periodicity two-yearly recurring course Language of instruction English

 Abstract This course provides an introduction to statistical Monte Carlo methods. This includes applications of simulations in various fields (Bayesian statistics, statistical mechanics, operations research, financial mathematics), algorithms for the generation of random variables (accept-reject, importance sampling), estimating the precision, variance reduction, introduction to Markov chain Monte Carlo. Objective Stochastic simulation (also called Monte Carlo method) is the experimental analysis of a stochastic model by implementing it on a computer. Probabilities and expected values can be approximated by averaging simulated values, and the central limit theorem gives an estimate of the error of this approximation. The course shows examples of the many applications of stochastic simulation and explains different algorithms used for simulation. These algorithms are illustrated with the statistical software R. Content Examples of simulations in different fields (computer science, statistics, statistical mechanics, operations research, financial mathematics). Generation of uniform random variables. Generation of random variables with arbitrary distributions (quantile transform, accept-reject, importance sampling), simulation of Gaussian processes and diffusions. The precision of simulations, methods for variance reduction. Introduction to Markov chains and Markov chain Monte Carlo (Metropolis-Hastings, Gibbs sampler, Hamiltonian Monte Carlo, reversible jump MCMC). Lecture notes A script will be available in English. Literature P. Glasserman, Monte Carlo Methods in Financial Engineering.Springer 2004.B. D. Ripley. Stochastic Simulation. Wiley, 1987.Ch. Robert, G. Casella. Monte Carlo Statistical Methods. Springer 2004 (2nd edition). Prerequisites / Notice Familiarity with basic concepts of probability theory (random variables, joint and conditional distributions, laws of large numbers and central limit theorem) will be assumed.