Due to the COVID-19 crisis, the information below is subject to change until September 13, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
30.0 h + 30.0 h
- LEPL1106 (or equivalent training in signals and systems)
- LEPL1108 (or equivalent training in probabilities and statistics)
The object of this course is to lead to a good understanding of stochastic processes, their most commonly used models and their properties, as well as the derivation of some of the most commonly used estimators for such processes : Wiener and Kalman filters, predictors and smoothers.
At the end of this learning unit, the student is able to :
1.1; 1.2; 1.3
3.1; 3.2; 3.3
At the end of this course, the students will be able to :
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
The course is subdivided into four parts/chapters:
- Probabilities, random variables, moments, change of variables.
- Stochastic processes, independence, stability, ergodicity, spectral representation, classical models of stochastic processes.
- Estimation (for random variables) : biais, variance, bounds, convergence, asymptotic properties, classical estimators.
- Estimation (for random processes) : filtering, prediction, smoothing, Wiener and Kalman estimators.
Learning will be based on courses interlaced with practical exercise sessions (exercises done in class or in the computer room using MATLAB). In addition, the training includes a project to be realized by groups of 2 or 3 students.
- Project during the course semester
- Other activities, such as quizzes and homework exercises, can be taken into account in the final grade.
Les notes de cours des co-titulaires sont disponibles.
Faculty or entity