This course is an introduction to fundamental tools in designing, conducting, and interpreting Monte Carlo simulations. Emphasis is on generic principles that are widely applicable in simulation, as opposed to detailed discussion of specific applications and/or software packages. At the completion of this course, it is expected that students will have the insight and understanding to critically evaluate or use many state-of-the-art methods in simulation. Topics covered include random number generation, simulation of Brownian motion and stochastic differential equations, output analysis for Monte Carlo simulations, variance reduction, Markov chain Monte Carlo, simulation-based estimation for dynamical (state-space) models, and, time permitting, sensitivity analysis and simulation-based optimization.
Course prerequisites: 
Linear algebra and a graduate-level statistics course such as 625.403
Course notes: 
This course serves as a complement to the 700-level course 625.744 Modeling, Simulation, and Monte Carlo. 625.433 Monte Carlo Methods and 625.744 emphasize different topics, and 625.744 is taught at a slightly more advanced level. 625.433 includes topics not covered in 625.744 such as simulation of Brownian motion and stochastic differential equations, general output analysis for Monte Carlo simulations, and general variance reduction. 625.744 includes greater emphasis on generic modeling issues (bias-variance tradeoff, etc.), simulation-based optimization of real-world processes, and optimal input selection.
Course instructor: 

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Course all programs: 
Applied and Computational Mathematics
Data Science
Financial Mathematics