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Gaussian Random Processes

Number of credits: 8 hp

Examiner: Mikhail Lifshits (web page)

Course literature: M.A.Lifshits: Lectures on Gaussian Processes, Springer, 2012. The book is available in an electronic version at the Linkoping University Library.

Course contents: Multivariate Gaussian distributions, examples of Gaussian random functions, infinite-dimensional distributions, kernel of Gaussian distribution (RKHS), isoperimetry, fundamental inequalities related to Gaussian measures, studies based on metric entropy, large deviation principle, series expansions for Gaussian processes, applications.

Organisation: Lectures combined with reading course and problem solving.

Examination: Solving assigned problems.

Prerequisites: Measure and integration. Probability: probability space, random variable, expectation, variance, distribution, density, random vectors in Euclidean space, covariance matrix, real and multivariate normal distributions. Random process, Wiener process. Basics of topology and functional analysis: open, closed, compact sets, Banach space, metric space, linear operators.

Page manager: karin.johansson@liu.se
Last updated: 2014-04-29