Markov Chain Monte Carlo
|Unit level:||Level 4|
|Teaching period(s):||Semester 2|
|Offered by||School of Mathematics|
|Available as a free choice unit?:||N
- MATH20701 - Probability 2 (Compulsory)
- MATH20802 - Statistical Methods (Compulsory)
- MATH48091 - Statistical Computing (Recommended)
Additional RequirementsMATH48122 pre-requisites
Students are not permitted to take, for credit, MATH48122 in an undergraduate programme and then MATH68122 in a postgraduate programme at the University of Manchester, as the courses are identical.
To introduce the student to computational Bayesian statistics, in particular Markov chain Monte Carlo (MCMC)
Since the late 1980's MCMC has been widely used in statistics and the range of its applications are ever increasing. This course will introduce MCMC methodology, in particular, the Metropolis-Hastings algorithm which is the basis for all MCMC. The implementation of MCMC will be discussed in detail with numerous examples.
On successful completion of this course unit students will be able to
- apply the Metropolis-Hastings algorithm and Gibbs sampler to standard problems;
- understand the issues involved with the implementation of MCMC;
- appreciate how computers can assist with Bayesian statistics.
Future topics requiring this course unit
- Other - 50%
- Written exam - 50%
Assessment Further Information
- Biweekly courseworks: 50%
- End of semester written examination: two hours 50%
- Introduction: Bayesian statistics, Markov chains. 
- Gibbs Sampler: data augmentation, burn-in, convergence. 
- Metropolis-Hastings algorithm: independent sampler, random walk Metropolis, scaling, multi-modality. 
- MCMC Issues: Monte Carlo Error, reparameterisation, hybrid algorithms, convergence diagnostics. 
- Perfect Simulation. 
- Reversible jump MCMC: unknown number of parameters. 
- Approximate Bayesian Computation: simulation based inference. 
- W. R. Gilks, S. Richarson and D. Spiegelhalter, Markov chain Monte Carlo methods in Practice, Chapman and Hall.
Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding. Coursework or in-class tests (where applicable) also provide an opportunity for students to receive feedback. Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.
- Lectures - 22 hours
- Tutorials - 22 hours
- Independent study hours - 106 hours