University of Galway

Course Module Information

Course Modules

Semester 1 | Credits: 5

An introductory course to Bayesian statistical modelling and analysis. Covers basic theory and methods of Bayesian model development and focuses on inference which is based on simulations (computations done in R). A prerequisite is a calculus based course in probability (at the level of ST2x3/MA235, for example). Prior experience studying statistics or regression analysis is helpful but not necessary.
(Language of instruction: English)

Learning Outcomes
  1. Determine likelihood and prior distributions as parts of a basic Bayesian model specification.
  2. Apply Bayes theorem to obtain posterior distribution of unknown random variables in the model.
  3. Derive posterior predictive distribution.
  4. Write simple R scripts implementing basic random sampling methods.
  5. Apply the basics of Markov chain theory to implement simulation algorithms for inference.
  6. Implement Gibbs sampler and Metropolis algorithm to obtain samples from posterior distributions.
  7. Compare and contrast basic Bayesian methods with classical statistics and realize advanatges and disadvantages of both.
  8. Develop simple Bayesian models for analysis of real world data sets.
Assessments
  • Department-based Assessment (100%)
Teachers
Reading List
  1. "Introduction to Bayesian Statistics" by William Bolstad
  2. "A First Course in Bayesian Statistical Methods" by Peter Hoff
  3. "Bayesian Data Analysis" by Gelman, Carlin, Stern and Rubin
    Publisher: Chapman & Hall / CRC
  4. "Introduction to Statistical Thought" by Michael Lavine,
The above information outlines module ST417: "Introduction to Bayesian Modelling" and is valid from 2021 onwards.
Note: Module offerings and details may be subject to change.