IB 509

Credit: 4 hours.

Introduction to statistical modeling from both likelihood and Bayesian perspectives. Focus is on science-driven, problem-specific design of statistical analyses for complex data. Topics include point estimation, interval estimation, model selection, regression, non-linear models, non-Gaussian models, hierarchical models, time-series analysis, spatial models, data assimilation, and statistical forecasting. Computational methods such as numerical optimization and Markov-Chain Monte-Carlo simulation are covered with a focus on hands-on application to real data. Course is designed around case-study problem sets using various statistical software packages. Examples are drawn primarily from the ecological/environmental sciences. Offered in alternate years.

Same as NRES 509. Prerequisite: MATH 220; CPSC 440 or STAT 400 or equivalent; or consent of instructor.