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BAYESIAN STATISTICS

ECON/GENBUS/STAT 775
Course Description

Introduces the theory, methods, and computational procedures needed to perform advanced Bayesian data analyses. Predictive and decision-theoretic motivations including subjective probability, risk, admissibility, and exchangeability; highlights key components of Bayesian analysis (i.e., prior, likelihood, posterior, and predictive distributions) within standard parametric models and advanced hierarchical and multilevel models; demonstrates the iterative process of model specification, implementation, criticism, and revision with applied case studies; implements computational techniques (e.g., Markov chain Monte Carlo, variational inference) in modern probabilistic programming languages.

Prerequisites
Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.79

5.78% from Historical

Completion Rate
96.77%

-1.61% from Historical

A Rate
83.87%

28.36% from Historical

Class Size
31

23.42% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

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