MAS472 Computational Inference

Note: This is an old module occurrence.

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Semester 2, 2019/20 10 Credits
Lecturer: Dr Alison Poulston uses MOLE Reading List
Aims Outcomes Teaching Methods Assessment

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.

Outline syllabus

  • Computational methods for likelihoods and likelihood theory.
  • Simulation. Generating techniques. Monte Carlo integration and variance reduction.
  • Bootstrapping.
  • 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.

Learning outcomes

By the end of the unit students should be able to demonstrate: 1. an understanding of the practice of statistical inference; 2. familiarity with the ideas, techniques and some uses of statistical simulation; 3. a knowledge of computational implementation of likelihood-based analyses; 4. a knowledge of modern computer-intensive statistical techniques

Teaching methods

Lectures, problem solving, computer practical sessions.

15 lectures, no tutorials, 5 practicals


One formal 2 hour written examination [100%]. Format: 3 questions from 3.

Reading list

Type Author(s) Title Library Blackwells Amazon
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.