Bayesian Statistics - MA883

Location Term Level Credits (ECTS) Current Convenor 2019-20
Canterbury
(version 2)
Autumn
View Timetable
7 15 (7.5) DR F Leisen

Pre-requisites

Co-requisite: MA881: Probability and Classical Inference

Restrictions

None

2019-20

Overview

Bayes Theorem for density functions; Conjugate models; Predictive distribution; Bayes estimates; Sampling density functions; Gibbs and Metropolis-Hastings samplers; Winbugs/OpenBUGS; Bayesian hierarchical models; Bayesian model choice; Objective priors; Exchangeability; Choice of priors; Applications of hierarchical models.

Details

This module appears in:


Contact hours

36 hours

Method of assessment

80% examination and 20% coursework

Indicative reading

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.

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes. On successfully completing the module students will be able to:

1 demonstrate systematic understanding of key aspects of Bayesian Statistics;
2 demonstrate the capability to solve complex problems using a very good 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 a range of concepts and principles in Bayesian Statistics in loosely defined contexts, showing good judgement in the selection and application of tools and techniques;
4 show judgement in the selection and application of R and WinBugs/OpenBugs.

The intended generic learning outcomes. On successfully completing the module students will be able to:

1 manage their own learning and make use of appropriate resources.
2 understand logical arguments, identifying the assumptions made and the conclusions drawn
3 communicate straightforward arguments and conclusions reasonably accurately and clearly
4 manage their time and use their organisational skills to plan and implement efficient and effective modes of working
5 solve problems relating to qualitative and quantitative information
6 make competent use of information technology skills such as R and WinBugs/OpenBugs, online resources (Moodle), internet communication.
7 communicate technical material competently
8 demonstrate an increased level of skill in numeracy and computation

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