Basic ideas and algorithms of Monte Carlo; simulation of basic stochastic processes. Brownian motion and the Poisson process, applications to financial modelling, queueing theory. Output analysis; variance reduction. Markov chain Monte Carlo methods; Gibbs sampling, simulated annealing and Metropolis-Hastings samplers with applications. Includes: Experiential Learning Activity Precludes additional credit for STAT 3555 (no longer offered). Prerequisite(s): STAT 3558, or a grade of B or higher in STAT 3508, or permission of the School. Lectures three hours a week, tutorial/laboratory one hour a week.
Basic ideas and algorithms of Monte Carlo; simulation of basic stochastic processes. Brownian motion and the Poisson process, applications to financial modelling, queueing theory. Output analysis; variance reduction. Markov chain Monte Carlo methods; Gibbs sampling, simulated annealing and Metropolis-Hastings samplers with applications. Includes: Experiential Learning Activity Precludes additional credit for STAT 3555 (no longer offered). Prerequisite(s): STAT 3558, or a grade of B or higher in STAT 3508, or permission of the School. Lectures three hours a week, tutorial/laboratory one hour a week.
