140.763.01 BAYESIAN METHODS II
- Gary Rosner
- Robert Scharpf
Builds upon the foundation laid in Bayesian Methods I (140.762). Discusses further current approaches to Bayesian modeling and computation in statistics. Describes and develops models of increasing complexity, including linear regression, generalized linear mixed effects, and hierarchical models. Acquaints students to advanced tools for fitting Bayesian models, including non-conjugate prior models. Includes examples of real statistical analyses.
Upon successfully completing this course, students will be able to: 1) develop Bayesian models for combining information across data sources; 2) create Winbugs program to run analyses; 3) calculate posterior distributions on parameters of scientific interest; 4) conduct Bayesian analyses of complex data sets.
Upon successfully completing this course, students will be able to:
develop Bayesian models for the analysis of complex problems, including repeated measurement data and latent data models;
create computer programs to run analyses;
calculate posterior distributions of parameters of scientific interest;
conduct Bayesian analyses of complex data sets.
- Tuesday 1:30 - 2:50
- Thursday 1:30 - 2:50