Prerequisite/Recommended prerequisite for
participation in the module
The module builds on knowledge obtained in the modules Probability
and statistics, Linear algebra and Programming.
Content, progress and pedagogy of the
- 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.)
- Be able to apply the stochastic processes taught in the course
to model real random mechanisms occurring in engineering
- Be able to simulate stochastic processes using a standard
- 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.
- 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 § 17.
|Name of exam||Stochastic Processes|
|Type of exam|
Written or oral exam
|Assessment||7-point grading scale|
|Type of grading||Internal examination|
|Criteria of assessment||The criteria of assessment are stated in the Examination