LMAT1271 - calcul des probabilités et analyse statistique.

LMAT1322 - Théorie de la mesure.

*The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.*

Probability spaces. Modes of convergence of sequences of random variables. Convergence in distribution.

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1 | Contribution of the course to learning outcomes in the Bachelor in Mathematics programme. By the end of this activity, students will have made progress in: - Recognise and understand a basic foundation of mathematics. ¿- Choose and use the basic tools of calculation to solve mathematical problems. ¿- Recognise the fundamental concepts of important current mathematical theories. - Show evidence of abstract thinking and of a critical spirit. ¿- Argue within the context of the axiomatic method Recognise the key arguments and the structure of a proof. ¿- Construct and draw up a proof independently. ¿- Evaluate the rigour of a mathematical or logical argument and identify any possible flaws in it. ¿- Distinguish between the intuition and the validity of a result and the different levels of rigorous understanding of this same result.
Learning outcomes specific to the course. By the end of this activity, students will be able to: - To work with probabily measures, random variables and their distributions in an abstract framework. - Prove and apply the convergence of a sequence of random variables : almost surely, in probability and in distribution. - Prove and apply the independence of a family of sigma-fields or random variables. - Make connections between probability theory and other branches of mathematics, in particular measure theory, complex analysis and functional analysis. |

*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 comprises three parts. The first one treats probability spaces seen as measure spaces with total mass equal to unity. The second part is about different modes of convergence of sequences of random variables, the main result being the strong law of large numbers. The subject of the third and final part is convergence in distribution, culminating in the central limit theorem.

The following concepts are treated :

- Probability spaces

- Random variables

- Expectation

- Convergence of random variables

- Independence

- Law of large numbers

- Convergence in distribution

- Characteristic functions

- Central limit theorem

In a typical lecture, the teacher first gives an overview of a chapter, motivating and giving context to the mathematical definitions and results. Students are then invited to read and study the chapter in detail and to solve the questions in the text. During this stage, the teacher interacts with the students individually or in small groups.

The exercises to be solved in the tutorials are announced in advance, allowing the students to prepare themselves.

During the course, some tests are organized allowing the students not only to practice their skills but also to get a joker for some of the exam questions.

- ' Syllabus "LMAT1371 - Probabilités" (J. Segers)

- P. Billingsley (1995): "Probability and Measure", Wiley, New York.

- • Syllabus "LMAT1371 - Probabilités" (J. Segers)

**MATH**