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
- 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 the introduction to Chapter 3.