Postgraduate Course: Statistical Theory (MATH11085)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | - 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. |
| Course description |
Not entered
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2015/16, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 6,
Supervised Practical/Workshop/Studio Hours 1,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
67 )
|
| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
See 'Breakdown of Assessment Methods' and 'Additional Notes', above. |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | MATH11085 Statistical Theory | 2:00 | |
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.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | STh |
Contacts
| Course organiser | Dr Natalia Bochkina
Tel: 0131 650 8597
Email: n.bochkina@ed.ac.uk |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: f.c.reid@ed.ac.uk |
|
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