Course Contents:
Advanced random number generation/testing, advanced input/output analysis, bootstrapping, advanced variance reduction methods, importance sampling, optimal computing budget allocation, rare-event simulation, Agent-Based Simulation, Monte Carlo Markov Chains, System Dynamics methodology, distributed & parallel simulation, validation and verification, meta modeling, design of simulation experiments, stochastic optimization using simulation, and other state-of-the-art techniques and methods in simulation.
References:
1. S. Asmussen, and P.W. Glynn, P.W., 2007, Stochastic Simulation: Algorithms and Analysis, Springer.
2. J.P.C. Kleijnen, 2008, Design and Analysis of Simulation Experiments, Springer
3. S.M. Ross, 2002, Simulation, 3rd ed., AcademicPress.
4. M. North, and C. Macal, 2007, Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation, Oxford University Press.