STA 650 Topics in Statistics (Applied Bayesian Statistics) (3) Bayesian models have increased flexibility and ease of interpretation as compared to frequentist methodolgy. As a result of advances in computing power and software development Bayes analyses has become much more popular. This course introduces Bayesian methods, demonstrates their usefulness in applied settings and shows how they can be implemented.
Objectives
  1. To understand the basic framework of the Bayesian approach to solve real-life problems
  2. To understand various statistical models
  3. To understand the reasoning, implementation, and implications of using prior information
Code 1
predictive densities
1-way anova
Rat tumor data
Rolling dice
More dice
A regression example
A logistic regression example
A logistic regression WinBUGS code
A logistic regression SAS code
Exponential family
Non-linear regression
Intro Log-linear models
Multivariate normal
ED50 multinomial regression

Last updated November 20, 2008