杏吧论坛

Dr Miltiadis Mavrakakis

This course is available on the BSc in Data Science, BSc in Mathematics with Data Science and BSc in Mathematics, Statistics and Business. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

Students must have completed Elementary Statistical Theory (ST102) and Mathematical Methods (MA100).

Students must have completed one of the following two combinations of courses: (a) ST102 and MA100, or (b) MA107 and ST109 and EC1C1. 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.

This course will be delivered through a combination of classes and lectures totalling a minimum of 30 hours across Autumn Term. In addition to these, there will be (optional) weekly workshops to help with homework assignments. This course includes a reading week in Week 6 of Autumn Term.

Students will be expected to produce 2 pieces of coursework in the AT.

These are exam-style class tests.

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: 2 hours) in the January exam period.