BAYESIAN METHODS I Syllabus
Course 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
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.
Biostatistics students interested in the application of Bayesian methods, as well as quantitatively-oriented students from other departments interested in Bayesian inference.
Methods of Assessment
Grading Policy: Two problem sets (30% for each one) and one take-home exam (40%)
Grading Restrictions: Letter grade
Biostatistics 140.651 and 140.652, or instructor consent
Academic Ethics Code
The code, discussed in the Policy and Procedure Memorandum for Students, March 31, 2002, will be adhered to in this class:
Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.
Disability Support Services
If you are a student with a documented disability who requires an academic accommodation, please contact Betty H. Addison in the Office of Student Life Services: firstname.lastname@example.org, 410-955-3034, or 2017 E. Monument Street.