Modulets indhold, forløb og pædagogik

Læringsmål

Viden

• Have knowledge about the theoretical framework in which stochastic processes are defined.
• Be able to understand the properties of the stochastic processes introduced in the course, such as wide-sense stationary (WSS) processes, Auto Regressive Moving Average (ARMA) processes, Markov models, and Poisson point processes.
• Be able to understand how WSS processes are transformed by linear time-invariant systems.
• Be able to understand the theoretical context around the introduced estimation and detection methods ((non-parametric and parametric) spectral estimation, Linear Minimum Mean Square Error (LMMSE) estimation, Wiener filter, Kalman filter, detection of signals, ARMA estimation, etc.)

Færdigheder

• Be able to apply the stochastic processes taught in the course to model real random mechanisms occurring in engineering problems.
• Be able to simulate stochastic processes using a standard programming language.
• Be able to apply the taught estimation and detection methods to solve engineering problems dealing with random mechanisms.
• Be able to evaluate the performances of the introduced estimation and detection methods.

Kompetencer

• Have the appropriate “engineering” intuition of the basic concepts and results related to stochastic processes that allow – for a particular engineering problem involving randomness – to design an appropriate model, derive solutions, assess the performance of these solutions, and possibly modify the model, and all subsequent analysis steps, if necessary.

Undervisningsform

Forelæsninger med tilhørende opgaveregning.

Omfang og forventet arbejdsindsats

Kursusmodulets omfang er 5 ECTS svarende til 137,5 timers studieindsats.

Eksamen

Prøver

Yderligere informationer

Kontakt venligst Studienævn for Matematiske Fag - studyboard@math.aau.dk