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%
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages
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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