STAT 251
Introduction to Bayesian Statistics
Statistics
College of Physical and Mathematical Sciences
Course Description
The scientific method; conditional probability; Bayes' Theorem; conjugate distributions: Beta-binomial, Poisson-gamma, normal-normal; Gibbs sampling.
When Taught
Fall and Winter
Grade Rule
Grade Rule 8: A, B, C, D, E, I (Standard grade rule)
Min
3
Fixed
3
Fixed
3
Fixed
0
Title
Code in R
Learning Outcome
Code in R a Gibbs sampler and/or Metropolis sampler for a simple non-conjugate posterior distributions
Title
Bayesian Analysis
Learning Outcome
Interpret and explain the results of Bayesian analysis
Title
Conjugate Priors, Binomial, and Poisson Distributions
Learning Outcome
Identify the conjugate priors of the normal (mean and variance), binomial, and Poisson distributions and derive the respective posterior distributions
Title
Gibbs and Metropolis Samplers
Learning Outcome
Explain why Gibbs and Metropolis samplers work and when they are appropriate to use
Title
Bayes' Theorem
Learning Outcome
Explain how conditional probability and Bayes' Theorem relate to the analysis of data via the Bayesian paradigm
