140.763.01
Bayesian Methods II
Location
East Baltimore
Note: Due to the COVID-19 Pandemic, this
course was held in a virtual/online format.
Term
4th Term
Department
Biostatistics
Credit(s)
3
Academic Year
2020 - 2021
Instruction Method
TBD
Tu, Th, 1:30 - 2:50pm
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Course Instructor(s)
Gary Rosner
Contact Name
Frequency Schedule
Every Other Year
Resources
Prerequisite
140.653-4
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