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Learn the language and basic concepts of probability.
Provide tools for modeling randomness.
Use of a simulation and statistical software: R.
This course will serve as a basis for the study of systems involving random phenomena encountered in most fields of engineering.
This is a prerequisite for courses like Probability for Computer Science and Principles and Meythods for Statistics.
Use and counting of sets: applications to game's theory.
Conditional probabilities, Bayes formula, Base rate fallacy.
Random variables, distributions, mean, variance and standart deviation.
Usual distributions : Bernoulli, Binomial, Geometric, Poisson, Uniform, Exponential, Gamma, Normal
Joined distibutions, Covariance, Correlation and Independence.
Simulation: by inversion, rejection method, law of large numbers, Monte Carlo method
Basic notions in mathematics.
Two partial examinations (E1 & E2).
Continuous assesment : CC
# Calcul de la note de chaque période remontée à la scolarité:
# Calcul des notes des deux bilans:
NB1 = NP1
NB2 = NP3
# Calcul des notes pour les jurys session 1 et 2
NFS1 = N1
NR = N2
NFS2 = NR
The course exists in the following branches:
Course ID : 3MM1PA
Course language(s):
The course is attached to the following structures:
You can find this course among all other courses.
Introduction to Probability and Statistics, MIT Open Courseware
G. Grimmett, Probability Theory and Examples. Oxford.
Date of update January 15, 2017