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 1, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 73 )
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.