Please see the full Module Specifications for background information relating to all of the APTS modules, including how to interpret the information below.
Aims: This module will introduce various computationally-intensive methods and their background theory, including
material on simulation-based approaches such as Markov-chain Monte Carlo (MCMC) and the bootstrap, and on strategies for handling large datasets. The different methods will be illustrated by applications.
Learning outcomes: After taking this module, students will have a working appreciation of MCMC, the bootstrap and other simulation-based methods and of their limitations, and have some experience of implementing them for simple examples.
Prerequisites: Preparation for this module should include a review of:
- familiarity with basic types of convergence of random variables: in probability, almost sure and in distribution;
- relevant basic material on statistical modelling (for which the earlier APTS module 'Statistical Modelling' would be advantageous);
- basic Markov chains (as for the 'Applied Stochastic Processes' module);
- basic knowledge of programming in a high-level language such as R (R will be used for case studies and exercises).
- Overview of simulation-based inference; Monte Carlo testing.
- Basic theory of bootstrap methods; practical considerations; limitations.
- Basic theory of MCMC; types of MCMC samplers; assessment of convergence/mixing; other practical considerations; case studies.
Assessment: Exercises set by the module leader, which will include some practical simulation.