Skip Navigation

Course Directory

140.763.01
Bayesian Methods II

Location:
East Baltimore
Term:
4th term
Department:
Biostatistics
Credits:
3 credits
Academic Year:
2022 - 2023
Instruction Method:
TBD
Class Times:
  • Tu Th,  1:30 - 2:50pm
Auditors Allowed:
Yes, with instructor consent
Undergrads Allowed:
No
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructors:
Contact:
Robert Scharpf
Frequency Schedule:
Every Other Year
Next Offered:
2024 - 2025
Resources:
Prerequisite:

140.653-4

Description:

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 based on linear regression, generalized linear mixed effects, and hierarchical models. Acquaints students with advanced tools for fitting Bayesian models, including non-conjugate prior models. Includes examples of real statistical analyses.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Develop Bayesian models for the analysis of complex problems, including repeated measurement data and latent data models
  2. Create computer programs to run analyses
  3. Calculate posterior distributions of parameters of scientific interest
  4. Conduct Bayesian analyses of complex data sets
Methods of Assessment:

Grades will be based on 3 homeworks and a class project.

Instructor Consent:

No consent required