Dr Milt Mavrakakis

This course is compulsory on the BSc in Actuarial Science, BSc in Actuarial Science (with a Placement Year), BSc in Data Science, BSc in Economics and Data Science, BSc in Financial Mathematics and Statistics, BSc in Mathematics with Data Science and BSc in Mathematics, Statistics and Business. This course is available on the BSc in Econometrics and Mathematical Economics, BSc in Mathematics and Economics, BSc in Mathematics with Economics, Erasmus Reciprocal Programme of Study and Exchange Programme for Students from University of California, Berkeley. This course is freely available as an outside option to students on other programmes where regulations permit. It does not require permission. This course is freely available to General Course students. It does not require permission.

Pre-requisites:

Before taking this course, students must have completed: (MA100 and ST102) or (EC1C1 and MA107 and ST109).

Additional requisites:

Equivalent combinations may be accepted at the lecturer's discretion.

The course covers the probability and distribution theory needed for advanced courses in statistics and econometrics.:

Topics covered: Probability. Conditional probability and independence. Random variables and their distributions. Moments and generating functions. Transformations. Sequences of random variables and convergence. Multivariate distributions. Joint and marginal distributions. Expectation and joint moments. Independence. Multivariate transformations. Sums of random variables. Conditional distributions. Conditional moments. Hierarchies and mixtures. Random sums.

10 hours of seminars, 10 hours of help session and 10 hours of lectures in the Autumn Term.

This course has a reading week in Week 6 of Autumn Term.

Students will be expected to produce 9 pieces of weekly homework, which will be returned with feedback. If there is sufficient student demand, there will be the option of an exam-style class test.

M C Mavrakakis & J Penzer, Probability and Statistical Inference: From Basic Principles to Advanced Models (primary reading)
G C Casella & R L Berger, Statistical Inference (very useful as a reference)

Exam (100%), duration: 120 Minutes in the January exam period.