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Department: Biostatistics
Term: 4th term
Credits: 3 credits
Contact: Gary Rosner
Academic Year: 2012 - 2013
Course Instructors:

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.

Learning Objective(s):
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.

Methods of Assessment: Student evaluation based on homework and a final project.
Location: East Baltimore
Class Times:
  • Tuesday 1:30 - 2:50
  • Thursday 1:30 - 2:50
Enrollment Minimum: 5
Instructor Consent: No consent required


Auditors Allowed: Yes, with instructor consent
Grading Restriction: Letter Grade or Pass/Fail
Frequency Schedule: Every Other Year
Next Offered: 2016-2017