Spring 2019 - STAT 460 D100
Bayesian Statistics (3)
Class Number: 3452
Delivery Method: In Person
The Bayesian approach to statistics is an alternative and increasingly popular way of quantifying uncertainty in the presence of data. This course considers comparative statistical inference, prior distributions, Bayesian computation, and applications. Quantitative.
1. The basics:
the Bayesian paradigm
comparative statistical inference
discrete mass priors
Markov chain Monte Carlo
4. Other topics as time permits:
testing via Bayes factors
interval and point estimation
elementary decision theory
- Participation 15%
- Assignments 20%
- Midterm 20%
- Final Exam 45%
Above grading is subject to change.
-Bayes and Empirical Bayes Methdos for Data Analysis (Carlin &Louis)
-Bayesian Data Analysis (Gelman, Carlin, Stern & Rubin)
Department Undergraduate Notes:
Students with Disabilites:
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