MA2PT1NU-Probability Theory

Module Provider: Mathematics and Statistics
Number of credits: 10 [5 ECTS credits]
Level:5
Terms in which taught: Autumn term module
Pre-requisites: MA0FMNU Foundations of Mathematics and ST1PSNU Probability and Statistics
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2020/1

Module Convenor: Dr Jeroen Wouters

Email: j.wouters@reading.ac.uk

Type of module:

Summary module description:

The module rigorously introduces basic concepts of probability from a mathematical perspective. It aims to equip the students with a basic knowledge in probability which will reveal the interplay between probability theory and fundamental areas of mathematics, will allow students to formulate general real or abstract problems in a probabilistic model and will unravel the fundamentals which statistical methods are built on. In more detail the module will be developed around the concepts of probability distributions, random variables, independence, sums of random variables, limit laws and their application (Central Limit Theorem and laws of large numbers), and structures that depend on the present to study the future evolution of stochastic phenomena (Markov chains).

The Module lead at NUIST is Dr Fatma Songul Ozesenli Tetikoglu

Aims:

This module aims to introduce students to some of the fundamental concepts and results of probability. It covers random variables together with probability distributions as the fundamental objects of probability theory, the concept of dependence/independence, which lead then to fundamental asymptotic results as well as a first introduction of stochastic processes such as Markov chains.

Assessable learning outcomes:

By the end of the module the students are expected to be able to:

• Identify and demonstrate understanding of the main concepts and definitions in probability theory;


• Without the help of notes to state all and prove some of the main results;


• Identify and formulate problems in terms of probability and solve them to buil d up a simple stochastic model;


• Use the main results to do various approximations.

Additional outcomes:

Outline content:

• Joint distributions


• Markov chains and conditional independence


• Inference of Markov chain


• Stationary distributions


• Graphical models


• Conditional independence from graphs


• Hidden Markov models and filtering


• Kalman filtering and Bayesian inference


• Hypothesis testing


• Moment generating functions


• Transforming random variables, tra nsformations of 2 variables


• Chebeyshev’s inequality and convergence in probability


• The weak law of large numbers


• The normal distribution


• Chi-square and students t-distribution

Brief description of teaching and learning methods:

Lectures supported by tutorials, practical and problem sheets.

Contact hours:

	Autumn	Spring	Summer
Lectures	48
Guided independent study:	52
Total hours by term	100	0	0
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Written exam	70
Written assignment including essay	30

Summative assessment- Examinations:

2 hours

Summative assessment- Coursework and in-class tests:

One examination and a number of assignments

Formative assessment methods:

Problem sheets.

Penalties for late submission:

The Module Convenor will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day[1] (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

A mark of 40% overall.

Reassessment arrangements:

One examination paper of 2 hours duration in August/September - the resit module mark will be the higher of the exam mark (100% exam) and the exam mark plus previous coursework marks (70% exam, 30% coursework).

Additional Costs (specified where applicable):

Last updated: 17 April 2020