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Department: Biostatistics
Term: 3rd term
Credits: 3 credits
Contact: Robert Scharpf
Academic Year: 2012 - 2013
Course Instructors:

Illustrates current approaches to Bayesian modeling and computation in statistics. Describes simple familiar models, such as those based on normal and binomial distributions, to illustrate concepts such as conjugate and noninformative prior distributions. Discusses aspects of modern Bayesian computational methods, including Markov Chain Monte Carlo methods (Gibbs' sampler) and their implementation and monitoring. Bayesian Methods I is the first term of a two term sequence. The second term offering, Bayesian Methods II (140.763), develops models of increasing complexity, including linear regression, generalized linear mixed effects, and hierarchical models.

Learning Objective(s):
Upon successfully completing this course, students will be able to:
explain the difference between the Bayesian approach to statistical inference and other approaches
develop Bayesian models for combining information across data sources
write and implement programs to run analyses
evaluate the influence of alternative prior models on posterior inference
plot and interpret posterior distributions for parameters of scientific interest

Methods of Assessment: Two problem sets (30% for each one) and one take-home exam (40%)
Location: East Baltimore
Class Times:
  • Tuesday 1:30 - 2:50
  • Thursday 1:30 - 2:50
Enrollment Minimum: 10
Enrollment Maximum: 40
Instructor Consent: No consent required

Biostatistics 140.651 and 140.652, or instructor consent

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