course focuses on the theoretical foundations of simulation methodology
and its recent advances with an emphasis on stochastic processes.
Topics include: generating random objects, output analysis
(autoregressive, regenerative, spectral, and stationary times series
methods), variance reduction techniques (antithetic variable, common
random numbers, control variables), Markov chain Monte Carlo (MCMC),
rare-event simulation techniques, stochastic optimization (likelihood
ratio method, perturbation analysis, stochastic approximation), sampling
of stochastic differential equation, exact simulation and prefect
sampling. Some of the motivating applications that will be discussed are
drawn from the domain of finance and risk management, insurance
modeling, and queuing networks.