4.00 credits
30.0 h + 15.0 h
Q1
Teacher(s)
Bogaert Patrick;
Language
French
Prerequisites
Préalabre : LBIR1110
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.
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.
Main themes
Introduction to the calculus of probability - Discrete and continuous random variables: probability and probability density functions, expectations, variance and other statistical properties - Principal statistical distributions - Couples of random variables and random vectors: joint, marginal and conditional distributions, independence, covariance and correlation, expectations and conditional variance - Introduction to statistics - Notions concerning estimators and estimator properties - Inference about the mean and variance: estimators, sample distributions - Notions of one-mean-confidence intervals.
Learning outcomes
At the end of this learning unit, the student is able to : | |
1 |
a. Contribution of this activity to the learning outcomes referential : 1.1, 2.1 b. Specific formulation of the learning outcomes for this activity A the end of this activity, the student is able to : · Name, describe and explain the theoretical concepts underlying the probability theory; · Use the mathematical expressions in a formal way and by using rigorous notations in order to deduce new expressions or requested theoretical results; · Translate mathematically textual statements using a rigorous mathematical and probabilistic framework by relying on appropriate concepts and theoretical tools; · Solve an applied problem by using a deductive approach that relies on a correct use of well identified properties and expressions; · Validate the internal consistency of the mathematical expressions and results based on theoretical properties and logical constraints that are induced by the probabilistic framework; |
Content
Notion of event and probability - Major theorems of probability calculus. Discrete and continuous random variables: probability and probability density functions, expectations, variance and other statistical properties. Major univariate statistical distributions. Couples of random variables and random vectors: joint, marginal and conditional distributions, independence, covariance and correlation, expectations and conditional variance. Introduction to random numbers and their applications.
Teaching methods
Regular courses and supervised practical exercises
Evaluation methods
Evaluation: Open book written examination (only with the original material). The examination is composed of exercises to be solved. Its duration is about 3 hours.
Other information
The course relies on a book which is considered as mandatory and must be bought :
P. Bogaert (2020). Probabilités pour scientifiques et ingénieurs (2nd ed). Editions De Boeck
P. Bogaert (2020). Probabilités pour scientifiques et ingénieurs (2nd ed). Editions De Boeck
Online resources
Moodle
Teaching materials
- P. Bogaert (2020). Probabilités pour scientifiques et ingénieurs (2ème éd). Editions De Boeck.
Faculty or entity
AGRO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Data Science : Statistic
Master [120] in Environmental Science and Management
Interdisciplinary Advanced Master in Science and Management of the Environment and Sustainable Development
Minor in Statistics, Actuarial Sciences and Data Sciences