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Degree Regulations & Programmes of Study 2010/2011
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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Theory of Statistical Inference (MATH10028)

Course Outline
School	School of Mathematics	College	College of Science and Engineering
Course type	Standard	Availability	Available to all students
Credit level (Normal year taken)	SCQF Level 10 (Year 4 Undergraduate)	Credits	10
Home subject area	Mathematics	Other subject area	Specialist Mathematics & Statistics (Honours)
Course website	http://student.maths.ed.ac.uk
Course description	Course for final year students in Honours programmes in Statistics. Parametric families and likelihood. Sufficiency, Neyman factorisation, minimal sufficiency, joint sufficiency, Bayesian sufficiency. Estimation, minimum variance unbiased estimators, Cramr-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.

Entry Requirements
Pre-requisites	Students MUST have passed: Foundations of Calculus (MATH08005) AND Several Variable Calculus (MATH08006) AND Linear Algebra (MATH08007) AND Methods of Applied Mathematics (MATH08035) AND Probability (Year 2) (MATH08008) AND Statistics (Year 2) (MATH08051)	Co-requisites
Prohibited Combinations	Students MUST NOT also be taking Statistics (Year 3) (MATH08052)	Other requirements	None
Additional Costs	None

Information for Visiting Students
Pre-requisites	None
Prospectus website	http://www.ed.ac.uk/studying/visiting-exchange/courses

Course Delivery Information

Delivery period: 2010/11 Semester 1, Available to all students (SV1)						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

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 problems.

Assessment Information
Examination only. Visiting Student Variant Assessment Examination (100%)
Please see Visiting Student Prospectus website for Visiting Student Assessment information

Special Arrangements
Not entered

Contacts
Course organiser	Dr Liam O'Carroll Tel: (0131 6)50 5070 Email: L.O'Carroll@ed.ac.uk	Course secretary	Mrs Alison Fairgrieve Tel: (0131 6)50 6427 Email: Alison.Fairgrieve@ed.ac.uk

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copyright 2010 The University of Edinburgh - 1 September 2010 6:18 am