140.763.01
Bayesian Methods II
Cancelled
- Location:
- East Baltimore
- Term:
- 4th term
- Department:
- Biostatistics
- Credits:
- 3 credits
- Academic Year:
- 2022 - 2023
- Instruction Method:
- In-person
- 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:
-
- Robert Scharpf
- Gary Rosner
- Contact:
- Robert Scharpf
- Frequency Schedule:
- Every Other Year
- 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:
- 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:
Grades will be based on 3 homeworks and a class project.
- Instructor Consent:
No consent required