Probabilistic modelisation - 3EU6MPA6

Informations générales

  • Number of hours

    • Lectures 16.0
    • Projects -
    • Tutorials 16.0
    • Internship -
    • Laboratory works -

    ECTS

    ECTS 2.5

Goal(s)

Understand the fundamental mathematical concepts for modeling and analyzing random phenomena. Be able to implement a suitable statistical model and infer model parameters from statistical data. Understand the challenges of this modeling: decision support, risk quantification, data science/AI.

Responsible(s)

Antoine VEZIER

Content(s)

Random Variables, Random vectors, Covariance matrix, gaussian vectors, conditional expectation, Convergence of Random Variable Sequences, Central Limit Theorem, Law of Large Numbers, Parametric Estimation, Linear Regression

Test

Calendar

The course exists in the following branches:

  • Curriculum - Master's Degree in Engineering ME - Semester 6
  • Curriculum - Master's Degree in Engineering SEM - Semester 6
  • Curriculum - Master's Degree in Engineering IEE - Semester 6
  • Curriculum - Master's Degree in Engineering IEN - Semester 6
  • Curriculum - Master's Degree in Engineering HOE - Semester 6
  • Curriculum - - Semester 6
  • Curriculum - Master's Degree in Engineering ASI - Semester 6
  • Curriculum - - Semester 6
see the course schedule for 2026-2027

Additional Information

Course ID : 3EU6MPA6
Course language(s): FR

You can find this course among all other courses.

Bibliography

Bernard CANDELPERGHER, Théorie des probabilités, Edition Calvage et Mounet, 2013
Olivier MARCHAL, Cours et exercices corrigés de statistiques inférentielles, Editions Ellipes, 2024