Stochastic Processes

2018/2019

Prerequisite/Recommended prerequisite for participation in the module

The module builds on knowledge of probability, statistics, linear algebra, Fourier theory, and programming

Content, progress and pedagogy of the module

Learning objectives

Knowledge

  • 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.)

Skills

  • 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.

Competences

  • 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.

Type of instruction

As described in the introduction to Chapter 3.

Exam

Exams

Name of examStochastic Processes
Type of exam
Written or oral exam
ECTS5
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentAs stated in Joint Programme Regulations
http:/​/​www.en.tech.aau.dk/​education-programmes/​Education+and+Programmes/​

Facts about the module

Danish titleStokastiske processer
Module codeESNCAK1K1F
Module typeCourse
Duration1 semester
SemesterAutumn
ECTS5
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module

Organisation

Study BoardStudy Board of Electronics and IT
FacultyTechnical Faculty of IT and Design