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Module Descriptor School of Computer Science and Statistics

Module CodeST3453
Module NameStochastic models in space and time I
Module Short Title
Semester TaughtMichaelmas
Contact Hours

Lecture hours:        36

Some of these lecture hours may be tutorials or computer labs.

Total hours:             36

Module PersonnelDr Jason Wyse
Learning Outcomes

Students will have ability to discuss and model simple versions of the following processes in time:

  • Everyday examples of stochastic processes
  • Understand and apply the Markov property
  • Describe long run properties of Markov processes
  • Deal with simple Markov processes in discrete time, continuous time and space
Learning Aims

Identify and use various stochastic processes for statistical modelling. Use application of stochastic processes to deal with a large selection of problems. Appreciate the importance of stochastic processes, and their mathematics.

Module Content

Specific topics addressed in this module include:

  • Examples of Stochastic processes.
  • The Markov property and Markov chains.
  • Stationarity
  • Poisson processes and their properties
  • Continuous time processes
  • Spatial stochastic processes (time permitting)
Recommended Reading List

The central text is

Ross, S. M.  Introduction to Probability Models, Academic Press.8th ed 2003 519.2 M94*7 ; 7th ed 519.2 M94*6 ; 6th ed  2002  PL-403-442 ; 5th ed 1993 PL-224-947. In the 6th ed, Ch 1-4, 6, 10 are relevant

Aspects of the following are relevant for MCMC (not entire course)

Robert, C.  and Casella, G. Monte Carlo Statistical Methods, Springer, 2nd ed.

Module Prerequisites

ST2351 and ST2352.

Assessment Details

Assesment 90% based on final exam and 10% based on assignments

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