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Module Descriptor School of Computer Science and Statistics

Module CodeST3454
Module NameStochastic Processes in Space and Time II
Module Short TitleStochastic Processes in Space and Time
Semester TaughtHilary Term
Contact HoursLecture hours: 33 Total hours:33
Module PersonnelDr. Rozenn Dahyot,
Learning OutcomesWhen students have successfully completed this module they should be able to: Define, describe and apply the different methods introduced in the course Program and analyse a dataset with these methods. Interpret the outputs of the data analysis performed by a computer statistics package.
Learning AimsThis course introduces different statistical modelling used for analysing stochastic processes defined in the spatial and/or time domains. These have many applications (e.g. engineering, finance).
Module ContentKalman 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
Recommended Reading ListGeostatistics for Environmental Scientists, R. Webster and M.A. Oliver, John Wiley and Sons 2001 Functional Data Analysis, J.O. Ramsay and B. W. Silverman, Springer 2006.
Module PrerequisitesSolid knowledge in mathematics and statistics required e.g. on Linear algebra, Integration and differentiation, expectation operator
Assessment Details100% Exam
Module Website
Academic Year of DataN/a