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Module ST3454: Stochastic Models in Space and Time II

Credit weighting (ECTS)
5 credits
Semester/term taught
Hilary term 2012-13
Contact Hours
11 weeks, 3 lectures including tutorials per week
Associate Professor Rozenn Dahyot
Learning Outcomes
On successful completion of this module students should be able to
  • Define and describe the different methods introduced in the course;
  • Program, analyse and select the best model;
  • Interpret output of data analysis performed by a computer statistics package;
Module Content
  • Kalman Filter;
  • State Space Models;
  • Brownian motion;
  • Orntein-Uhlenbeck Process;
  • Dirac function;
  • Kriging;
  • Spectral analysis;
  • Radon transform;
  • Functional data analysis (Kernel density estimates, Nadaraya-Watson estimator)
Module Prerequisite
Solid knowledge in mathematics and statistics required e.g. on Linear algebra, Integration and differentiation, expectation operator.
Recommended Reading
  • Linear Models for Multivariate, Time Series, and Spatial Data, R.Christensen, Springer 1991;
  • Multivariate Geostatistics - An Introduction with Applications, H.Wackernagel, Springer 2003
  • Statistics of Spatial Data, Noel Cressie, Wiley 1993
  • Geostatistics for Environmental Scientists, R. Webster and M.A. Oliver, John Wiley and Sons 2001
  • Time series Analysis with Applications in R, J.D. Cryer and K.S. Chan, Springer 2008
  • Functional Data Analysis, J.O. Ramsay and B.W. Silverman, Springer 2006

Assessment Detail

This module will be examined jointly with ST3453 in a 3-hour examination in Trinity term, except that those taking just one of the two modules will have a 2 hour examination.