MAS472 Computational Inference
|Semester 2, 2017/18||10 Credits|
|Lecturer:||Dr Richard Wilkinson||uses MOLE||Timetable||Reading List|
This unit aims to introduce the student to some of the powerful modern tools now available for statistical inference. The tools are largely based on the exploitation of modern computing power. They free the analyst from the distributional limitations of the past and they are widely applicable, both to traditional application areas of statistics and in new situations. The emphasis in the course will be on the practical utility of the methodology, though theoretical ideas will be introduced when necessary for understanding and use. Appropriate computer packages will be used to implement the methods.
Prerequisites: MAS364 or MAS464 (Bayesian Statistics)
No other modules have this module as a prerequisite.
- Computational methods for likelihoods and likelihood theory.
- Simulation. Generating techniques. Monte Carlo integration and variance reduction.
- Simulation and Monte Carlo testing. Randomization tests.
- To extend understanding of the practice of statistical inference.
- To familiarize the student with ideas, techniques and some uses of statistical simulation.
- To describe computational implementation of likelihood-based analyses.
- To introduce examples of modern computer-intensive statistical techniques.
Lectures, problem solving
20 lectures, no tutorials
One formal 2 hour written examination [85%]. Format: 3 questions from 4. Coursework [15%].
|B||Garthwaite, Jolliffe and Jones||Statistical Inference||519.43 (G)||Blackwells||Amazon|
|B||Kalbfleisch||Probability and Statistical Inference, Volume 2: Statistical Inference||519.2 (K)||Blackwells||Amazon|
|B||Morgan||Elements of Simulation||519.39 (M)||Blackwells||Amazon|
|B||Robert and Casella||Introducing Monte Carlo Methods with R||518.282(R)||Blackwells||Amazon|
(A = essential, B = recommended, C = background.)
Most books on reading lists should also be available from the Blackwells shop at Jessop West.