Bayesian Analysis (STAT40390)

Credits  7.5  Subject  Statistics & Actuarial Science 
Level  4  School  Mathematics and Statistics 
Semester  Spring  Module Coordinator  Dr Michael SalterTownshend 
Bayesian Analysis models all unknown quantities in a coherent probabilistic framework. Full probability distributions for model parameters conditional on observed data are derived. This module explores how this can be done, both algebraically and computationally. Understanding the Bayesian approach to inference is central and manipulation of conditional distributions is key. The free software package JAGS will be used to perform analysis on a range of statistical models, from simple to complex hierarchical models. Topics covered include: conditional probability, Bayes' Theorem, prior distributions, conjugacy, the likelihood principle, multiparameter problems, Bayesian hypothesis testing and model checking, methods for finding the posterior mode, Markov Chain Monte Carlo, advanced Bayesian modelling. Illustrative examples from the scientific literature will be used. As part of the course students are expected to complete a project worth 20% of their final grade.
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