Coursebooks 2017-2018

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Probability theory

MATH-432

Lecturer(s) :

Mountford Thomas

Language:

English

Summary

The course provides a rigourous measure theory based introduction to probability. We treat the various forms of convergence for random variables up to convergence in distribution.

Content

- general probability spaces, random variables and measurable functions, measures and probabilities

- expectation for a random variable and reminder of integration theory

- independence and the Borel-Cantelli lemmas

- strong and weak laws of large numbers

- central limit theorem

Learning Prerequisites

Recommended courses

First cycle, Advanced analysis A (measure theory)

Learning Outcomes

By the end of the course, the student must be able to:

Teaching methods

Ex cathedra lecture and exercises in the classroom

 

Assessment methods

Exam written

Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.

Supervision

Office hours No
Assistants No
Forum No

Resources

Bibliography

R. Durrett. Probability: theory and examples.

Ressources en bibliothèque
Websites

Prerequisite for

Probabilities, Stochastic process

In the programs

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14 MAA112MAA112  
14-15   
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     
 
      Lecture
      Exercise, TP
      Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German