MAS364 Bayesian Statistics
Note: Information for future academic years is provisional. Timetable information and teaching staff are especially likely to change, but other details may also be altered, some courses may not run at all, and other courses may be added.
|Semester 1, 2022/23||10 Credits|
|Lecturer:||Prof Paul Blackwell||Home page||Timetable||Reading List|
This unit develops the Bayesian approach to statistical inference. The Bayesian method is fundamentally different in philosophy from conventional frequentist/classical inference, and has been the subject of some controversy in the past. It is, however, becoming increasingly popular in many fields of applied statistics. This course will cover both the foundations of Bayesian statistics, including subjective probability, inference, and modern computational tools for practical inference problems, specifically Markov Chain Monte Carlo methods and Gibbs sampling. Applied Bayesian methods will be demonstrated in a series of case studies using the software package R.
Prerequisites: MAS223 (Statistical Inference and Modelling)
No other modules have this module as a prerequisite.
- Subjective probability.
- Inference using Bayes Theorem. Prior distributions. Exponential families. Conjugacy. Exchangeability.
- Predictive inference.
- Hierarchical models.
- Computation. Gibbs sampling. Metropolis-Hastings. Case studies.
- To extend understanding of the practice of statistical inference.
- To familiarize the student with the Bayesian approach to inference.
- To describe computational implementation of Bayesian analyses.
- Carry out Bayesian analysis for a range of standard statistical problems.
- Apply the Bayesian approach to straightforward novel situations.
- Use Bayesian computational software, e.g. R, for problems that are not analytically tractable.
20 lectures, no tutorials, 3 practicals
One formal 2 hour written examination [85%]. One project [15%] due by the end of the semester.
- The subjective interpretation of probability. Constructing subjective probabilities.
- Independence and exchangeability.
- Inference using Bayes Theorem. Discrete examples.
- Prior distributions. Exponential families. Conjugacy.
- Predictive distribution.
- Hierarchical models.
- Gibbs sampling, graphical models.
- MCMC using R.
- Case studies.
|B||DeGroot, M.H. and Schervish, M.J.||Probability and Statistics||Blackwells||Amazon|
|B||Gelman, Carlin, Stern and Rubin||Bayesian Data Analysis||519.42 (W)||Blackwells||Amazon|
|B||Lee||Bayesian Statistics: An Introduction||519.542 (L)||Blackwells||Amazon|
|B||Spiegelhalter, D.J., Abrams, K.R. and Myles J.P.||Bayesian Approaches to Clinical Health-Care Evaluation||Blackwells||Amazon|
(A = essential, B = recommended, C = background.)
Most books on reading lists should also be available from the Blackwells shop at Jessop West.