Stochastic Processes

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Description

Module Overview: Stochastic Processes (semester 2)

The first three lectures are devoted to Markov processes. These form the basis of most probabilistic modelling of physical and other processes. Elegant descriptions of their long-term behaviour make them particularly useful. They have further important applications in statistical inference.

A further three lectures are devoted to the stochastic modelling and simulation of physical processes. With the availability of extensive computing power, simulation techniques have become an important probabilistic and statistical tool, and there is now an extensive theory of probabilistic simulation.  One of these lectures focuses on the interplay between probability theory and graph theory.

The final four lectures are concerned with continuous-time stochastic processes and stochastic calculus. Again there are important applications to the modelling of physical and natural processes.

Lecturers:

• Mateusz Majka (Heriot-Watt University)
• Burak Buke (University of Edinburgh)
• Istvan Gyongy (University of Edinburgh)

Assessment

This module is assessed by two written assignments (to be set at least two weeks before the deadline), with deadlines on 7th March 2024 and 28th March 2024. Solutions to at least one of the assignments should be produced using LaTeX.

Prerequisites

Elements of mathematical analysis, linear algebra and combinatorics at undergraduate level. Probability theory, either at undergraduate level or from the Foundations of Probability module.