Probability and statistics (for IC)
Summary
A basic course in probability and statistics
Content
Revision of basic set theory and combinatorics.
Elementary probability: random experiment; probability space; conditional probability; independence.
Random variables: basic notions; density and mass functions; examples including Bernoulli, binomial, geometric, Poisson, uniform, normal; mean, variance, correlation and covariance; moment-generating function; joint distributions, conditional and marginal distributions; transformations.
Many random variables: notions of convergence; laws of large numbers; central limit theorem; delta method; applications.
Statistical inference: different types of estimator and their properties and comparison; confidence intervals; hypothesis testing; likelihood inference and statistical modelling; Bayesian inference and prediction; examples.
Learning Prerequisites
Required courses
Analyse I, II
Algèbre linéaire
Teaching methods
Ex cathedra lectures, exercises and problems
Assessment methods
Written exam
Resources
Notes/Handbook
A polycopié of the course notes, with the problems etc., will be available.
Moodle Link
Prerequisite for
Electrométrie, Théorie du signal, Télécommunications, Information et codage, Fiabilités, ...
In the programs
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Probability and statistics (for IC)
- Lecture: 4 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Probability and statistics (for IC)
- Lecture: 4 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Probability and statistics (for IC)
- Lecture: 4 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory