140.762.01
Bayesian Methods I
Location
East Baltimore
Term
3rd Term
Department
Biostatistics
Credit(s)
3
Academic Year
2024 - 2025
Instruction Method
In-person
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
Biostatistics 140.651 and 140.652, or instructor consent
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 Objectives
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
This course is evaluated as follows:
- 75% 6 homework assignments, each worth 12.5%
- 25% Final Exam