Stochastic Processes

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Description

Module Overview: Stochastic Processes (semester 2)

Time: Mondays, 15.30 - 17.30

Location for Maxwell Institute students: Bayes Centre (room 5.46)

Module Leader: TBC

Module Summary

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.

Assessment

This module is assessed by two written assignments (to be set at least two weeks before the deadline), each equally weighted and worth 50% of the Final Grade. Solutions to at least one of the assignments should be produced using LaTeX.

Assignment 1: TBC

Assignment 2: TBC