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Stationary stochastic processes

Official Course Description

LTH Course Description (SV) NF Course Description (SV) LTH Course Description (EN)

Description

Models for stochastic dependence. Concepts of description of stationary stochastic processes in the time domain: expectation, covariance, and cross-covariance functions. Concepts of description of stationary stochastic processes in the frequency domain: effect spectrum, cross spectrum. Special processes: Gaussian process, Wiener process, white noise, Gaussian fields in time and space. Stochastic processes in linear filters: relationships between in- and out-signals, auto regression and moving average (AR, MA, ARMA), derivation and integration of stochastic processes. The basics in statistical signal processing: estimation of expectations, covariance function, and spectrum. Application of linear filters: frequency analysis and optimal filters.

Coming Sessions

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Finished Sessions

Course Information

LTH Code:FMSF10
NF Code: MASC04
Credits:7.5
Level:Upper Basic Level
Language:English

Prerequisites

Mathematical statistics, basic course (Mandatory) Mathematical Statistics, Basic Course (Alt Mandatory) Mathematical Statistics, Basic Course (M) (Alt Mandatory) Mathematics - Systems and Transforms (Alt Mandatory) Mathematical Statistics, Basic Course (L,V) (Alt Mandatory) Mathematical Statistics (B,K,N,BME) (Alt Mandatory) Mathematical Statistics, Basic Course (W) (Alt Mandatory) Mathematical Statistics (Alt Mandatory)

Following courses

Monte Carlo methods for stochastic inference Non-linear Time Series Analysis Statistical image analysis Time series analysis Valuation of Derivative Assets DTU Course: Hidden Markov Models Examensarbete- kandidatexamen i matematisk statistik Valuation of Derivative Assets Spatial statistics with image analysis, Stationary and non-stationary Spectral Analysis

CEQ

CEQ - Stationary stochastic processes

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Last update: 2013-04-10

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