Mathematics - Probabilities
Mathematics - Probability theory 5JUCSN05 | ECTS | 3 | SEMESTER | 5 | |||||||||||||
lectures | classes / seminars | practical work | integrative teaching | independent work | |||||||||||||
10h | 20h | 0h | 0h | 30h | |||||||||||||
Language used | French |
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Course supervisor(s) | Coralie FRITSCH & Takéo TAKAHASHI | ||||||||||||||||
Key words | probability, measure theory, Lebesgue integral, random variable, law of large numbers, central limit theorem, Gaussian vectors | ||||||||||||||||
Prerequisites | basic knowledge of calculus, linear algebra, Riemann integral and elementary set theory | ||||||||||||||||
Overall objective | |||||||||||||||||
The objective of the course is to master classical notions in probability theory, in measure theory, and to know how to compute the law of some random variables | |||||||||||||||||
Course content and organisation | |||||||||||||||||
This course is concerned with classical notions in probability and measure theory, which include the following topics: 1. Probability spaces, random variable and expectation. 2. Discrete and absolutely continuous distributions, classical distributions, Law of the unconscious statistician. 3. Characterization of laws through the cumulative distribution and characteristic functions. Computation of the law of a random variable. 4. Random vectors, marginal laws, Fubini's theorem, Change of variable 5. Fatou's Lemma, Monotone convergence theorem, Dominated convergence theorem 6. Lp spaces, moments, variance, covariance, linear regression 7. Independance and Convolution 8. Different notions of convergence of random variables : in probability, in distribution, almost sure, in Lp. Borel Cantelli Lemma. 9. Gaussian vectors Students will also be provided with the basic tools to numerically simulate random variables in Python and Matlab. | |||||||||||||||||
Skills | |||||||||||||||||
Levels | Description and operational verbs | ||||||||||||||||
Know | The fundamental aspects of measure theory and the basic tools for computation of probability distributions | ||||||||||||||||
Understand | To understand the logic of probability theory and the way it applies to concrete calculous | ||||||||||||||||
Apply | To deploy calculus methods to resolve problems in probability, choosing among different tools to describe random behaviours | ||||||||||||||||
Analyse | To detect basic properties of random phenomena and to devise a mathematical strategy to analyse them | ||||||||||||||||
Summarise | To elaborate a well structured discussion about topics on random phenomena | ||||||||||||||||
Assess | To detect aberration in probability results and assess the validity of different property concerning random phenomena, as independance, convergence or integrability | ||||||||||||||||
Compliance with the United Nations Sustainable Development Goals | |||||||||||||||||
Evalution methods | |||||||||||||||||
Continuous assessment | Written test | Oral presentation / viva | Written report / project |