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Course Module Information
Course Modules
ST417: Introduction to Bayesian Modelling
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
- Determine likelihood and prior distributions as parts of a basic Bayesian model specification.
- Apply Bayes theorem to obtain posterior distribution of unknown random variables in the model.
- Derive posterior predictive distribution.
- Write simple R scripts implementing basic random sampling methods.
- Apply the basics of Markov chain theory to implement simulation algorithms for inference.
- Implement Gibbs sampler and Metropolis algorithm to obtain samples from posterior distributions.
- Compare and contrast basic Bayesian methods with classical statistics and realize advanatges and disadvantages of both.
- Develop simple Bayesian models for analysis of real world data sets.
Assessments
- Department-based Assessment (100%)
Teachers
- COLLETTE MCLOUGHLIN:
Research Profile |
Email
- ANDREW SIMPKIN:
Research Profile |
Email
Reading List
- "Introduction to Bayesian Statistics" by William Bolstad
- "A First Course in Bayesian Statistical Methods" by Peter Hoff
- "Bayesian Data Analysis" by Gelman, Carlin, Stern and Rubin
Publisher: Chapman & Hall / CRC - "Introduction to Statistical Thought" by Michael Lavine,
Note: Module offerings and details may be subject to change.