Applied Probability and Simulation Modeling

1 Unit · Graduate

This advanced modeling course begins by reviewing probability theory and models. Students learn principals of random number generation and Monte Carlo methods for classical and Bayesian statistics. They are introduced to applied probability models and stochastic processes, including Markov Chains, exploring applications in business and scientific research. Students work with open-source and proprietary systems, implementing discrete event and agent-based simulations. This is a case study and project-based course with an extensive programming component. Recommended prior course: MSDS 460-DL . (Required: (1) MSDS 420-DL or CIS 417-DL and (2) MSDS 422-DL or CIS 435-DL .)