Postgraduate Course: Statistical Theory (MATH11085)
|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
|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.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Course Delivery Information
|Academic year 2014/15, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 22,
Seminar/Tutorial Hours 1,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||See 'Breakdown of Assessment Methods' and 'Additional Notes', above.
||Hours & Minutes
|Main Exam Diet S1 (December)||MATH11085 Statistical Theory||2:00|
| 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.
|Graduate Attributes and Skills
|Course organiser||Dr Natalia Bochkina
Tel: 0131 650 8597
|Course secretary||Mrs Frances Reid
Tel: (0131 6)50 4883
© Copyright 2014 The University of Edinburgh - 12 January 2015 4:22 am