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January 2020

Applied Probability III

Go to this course in the University Course Planner.


Many processes in the real world involve some random variation superimposed on a deterministic structure. For example, the experiment of flipping a coin is best studied by treating the outcome as a random one. Mathematical probability has its origins in games of chance with dice and cards, originating in the fifteenth and sixteenth centuries. This course aims to provide a basic toolkit for modelling and analyzing discrete-time problems in which there is a significant probabilistic component. We will consider Markov chain examples in the course including population branching processes (with application to genetics), random walks (with application to games), and more general discrete time examples using Martingales.


to introduce students to the basic tools used by applied probabilists, and to give students a thorough grounding in the theory of discrete-time Markov chains on a countable state space. To provide a thorough grounding in discrete-time Markov chains on a countable state space. The student will obtain an in-depth knowledge of Markov chains both from a theoretical and a modelling viewpoint.


Topics covered are: basic probability and measure theory, discrete time Markov chains, hitting probabilities and hitting time theorems, population branching processes, inhomogeneous random walks on the line, solidarity properties and communicating classes, necessary and sufficient conditions for transience and positive recurrence, global balance, partial balance, reversibility, Martingales, stopping times and stopping theorems with a link to Brownian motion.

David Green
Lecturer for this course

Graduate attributes

Linkage future

The material covered in this unit is useful for APP MTH 3016 Telecommunications Systems Modelling III.

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References: a list will be provided in lectures