Statistical Modelling I

3 credits · Undergraduate Programs

An introduction to statistical computing and Bayesian modeling. Topics covered include random numbers generation, system/process simulation and evaluation, numerical integration, constrained and unconstrained optimization, Bayesian inference framework, single and multi-parameter models, regression models, Bayesian hierarchical modelling, Markov chain Monte Carlo. Prerequisite(s): Data Science 305, Mathematics 211, and 267.