Stationary Stochastic Processes

Official Course Description

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

Current Sessions

• Autumn, first half 2017 : Stand-alone Courses, Information and Communication Engineering Technologies, Computer Science and Engineering, Electrical Engineering, Engineering Physics, Industrial Engineering and Management, Surveying and Land Management, Master Programme in Wireless Communications, Master's Programme in Mathematics, Master's Program in Mathematical Statistics, Engineering Mathematics, Risk Management and Safety Engineering, Environmental Engineering

Finished Sessions