Postgraduate Course: Statistical Theory (MATH11085)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | - Statistical modelling and motivation.
- Parametric families and likelihood. Sufficiency, Neyman factorisation, minimal sufficiency, joint sufficiency, Bayesian sufficiency.
- Estimation, minimum variance unbiased estimators, Cramer-Rao lower bound, Bayes estimators. Hypothesis testing, pure significance tests, optimal tests, power, Neyman-Pearson lemma, uniformly most powerful tests.
- Confidence intervals, relationship to hypothesis testing, Bayesian credible intervals.
- Bayesian inference, conjugate prior distributions, predictive distributions.
- Markov chain Monte Carlo methods for Bayesian inference, and Gibbs sampling. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Course Delivery Information
|
| Delivery period: 2011/12 Semester 1, Not available to visiting students (SS1)
|
WebCT enabled: Yes |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| No Classes have been defined for this Course |
| First Class |
First class information not currently available |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
|
|
| Main Exam Diet S2 (April/May) | MSc Statistical Theory | 2:00 | | |
Summary of Intended Learning Outcomes
1. Knowledge of the theory of statistical inference.
2. Ability to prove and apply results concerning Frequentist and Bayesian inference.
3. Ability to develop theoretical arguments.
4. Familiarity with dealing with multiparameter statistical problems.
5. Knowledge of Markov chain Monte Carlo methods and Gibbs sampling. |
Assessment Information
| Examination 100% |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | STh |
Contacts
| Course organiser | Dr Bruce Worton
Tel: (0131 6)50 4884
Email: Bruce.Worton@ed.ac.uk |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: f.c.reid@ed.ac.uk |
|
© Copyright 2011 The University of Edinburgh - 16 January 2012 6:25 am
|
University Homepage