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Aug 24, 2024
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STAT 681 - Bayesian Statistics Units: 2 Bayes Theorem, subjective probability, conjugate priors, non-informative priors, posterior estimation, credible intervals, prediction, sensitivity analysis, comparison to classical procedures. MCMC, Gibbs sampling, hierarchical Bayesian models. Use of statistical software. Report writing.
Prerequisites: STAT 630. Equivalent Quarter Course: STAT 6550. Possible Instructional Methods: Entirely On-ground. Grading: ABC/NC grading only. Student Learning Outcomes - Upon successful completion of this course students will be able to: - Demonstrate the use of Bayes rule to compute posterior probabilities.
- Apply Bayesian hierarchical models to real data examples.
- Write R scripts using various R packages to use Bayesian models.
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