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STAT-F407

Stochastic models

academic year
2020-2021
The health crisis linked to the spread of Covid-19 constitutes grounds of force majeure constraining the University to adapt the assessment methods for some of the course units; Information on the new terms of these assessments will be provided to students through the usual information channels by December 4, 2020.

Course teacher(s)

Jennifer ALONSO GARCIA (Coordinator), Clément Cerovecki and Joost VERCRUYSSE

ECTS credits

5

Language(s) of instruction

english

Course content

The first part of the course is a review course on probability theory that will help the student to follow the second part of the course and other courses in stochastic calculus, probability and statistics.

In the second part of the course we study different types of processes:

  1. Discrete time processes: Martingales and Markov chains

  2. Continuous time processes: Markov processes, Poisson processes and Brownian motions.

Objectives (and/or specific learning outcomes)

There are two main objectives for this course. The first one is to provide some basic notions of probability theory that will help the student to follow the second part of the course together with other courses in stochastic calculus, probability and statistics.

In the second part of the course, the objective is to give the standard results on well-known processes such as Martingales, Markov processes and Brownian motions.

At the end of this course, the student will be able to

  • Follow various advanced courses in Probability, Statistics and Stochastic Calculus

  • deal with some aspects of simple processes

  • modelize some practical situations using processes

Teaching methods and learning activities

We give traditional lectures based on slides and exercises sessions.

Contribution to the teaching profile

Ce document (en dehors de cette partie) est rédigé uniquement en anglais puisque le cours se donne en anglais.

Contribution au profil d'enseignement:

  • S'approprier les concepts fondamentaux en probabilités et en statistique, théoriques ou appliquées.

  • Acquérir des notions avancées dans certains domaines des probabilités ou de la statistique.

  • Collecter des informations en vue d’établir un état de l’art d’un domaine de statistiques.

  • Etre capable de modéliser des données réelles et de les analyser par les méthodes statistiques classiques.

  • Choisir de façon adéquate l’analyse statistique qui convient au problème considéré.

  • Etre responsable de ses affirmations.

  • Pratiquer la critique relativement à la validité d’une affirmation.

Other information

Contacts

Clément Cerovecki and Jennifer Alonso García.

Evaluation

Method(s) of evaluation

  • Other

Final exam based on theory and exercises. You have to pass both parts at the same time.

Mark calculation method (including weighting of intermediary marks)

100% final exam

Language(s) of evaluation

  • english

Programmes