Analysis of scalar and vector-valued parameters; Bayesian linear models; Monte Carlo computational methods; prior elicitation; hypothesis testing and model selection; hierarchical models; selected advanced models; use of statistical packages such as WinBUGS, R or MATLAB. Prerequisites: MATH 221; STAT 408 or equivalent Credits 3. 3 Lecture Hours.
Analysis of scalar and vector-valued parameters; Bayesian linear models; Monte Carlo computational methods; prior elicitation; hypothesis testing and model selection; hierarchical models; selected advanced models; use of statistical packages such as WinBUGS, R or MATLAB. Prerequisites: MATH 221; STAT 408 or equivalent Credits 3. 3 Lecture Hours.
