ST740 - Bayesian Inference
- Prerequisites: ST522
- Term & Frequency: Every Fall
- Student Audience: PhD students in Statistics and related fields
- Credit: 3 credits
- Recent Texts: Christensen, Johnson, Branscum, Hanson (2011), Bayesian Ideas and Data Analysis, Chapman & Hall/CRC. Hoff (2009), A First Course in Bayesian
Statistical Methods, Springer.
- Recent Instructors: Brian Reich, Alyson Wilson, Subhashis Ghosal
- Background and Goals: Introduction to Bayesian inference; specifying prior distributions; conjugate priors, summarizing posterior information, predictive distributions, hierachical models, asymptotic consistency and asymptotic normality. Markov Chain Monte Carlo (MCMC) methods and the use of existing software (e.g., WinBUGS).
- Content: Prior distributions; Objective Bayes priors; Bayes rules; Gibbs sampling; Metropolis-Hastings sampling; Bayes factors; Semiparametric Bayesian methods; Model diagnostics.
- Alternatives: ST 540, Applied Bayesian Analysis
- Subsequent Courses: Advanced Bayesian Inference (taught as a special topics ST 790)
SP 2017 Sections:
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