Bayes Theorem for density functions; Conjugate models; Predictive distribution; Bayes estimates; Sampling density functions; Gibbs and Metropolis-Hastings samplers; Stan and Python; Bayesian hierarchical models; Bayesian model choice; Objective priors; Exchangeability; Choice of priors; Applications of hierarchical models.
Total contact hours: 36
Private study hours: 114
Total study hours: 150
Coursework 50%
Exam 50%
A.F.M. Smith and Bernardo, J.M. (1994). Bayesian Theory. Wiley.
A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson, A. Vehtari and D.B. Rubin (2014). Bayesian Data Analysis. 3rd Edition, Chapman & Hall/CRC Texts in Statistical Science.
D. Gamerman and H.F. Lopes (2006). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. 2nd Edition, Taylor and Francis.
On successfully completing this module students will be able to:
1) demonstrate systematic understanding of key aspects of Bayesian Statistics;
2) demonstrate the capability to deploy established approaches accurately to analyse and solve problems using a reasonable level of skill in calculation and manipulation of the material in the following areas: derivation of posterior distributions; computation of posterior summaries, including the predictive distribution; construction of Bayesian hierarchical models and their estimation using Markov chain Monte Carlo methods; critical evaluation and interpretation of software output
3) apply key aspects of Bayesian Statistics in well-defined contexts, showing judgement in the selection and application of tools and techniques;
4) show judgement in the selection and application of techniques in Stan and Python.
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