Probability - 3MM1PA
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Number of hours
- Lectures : 18.0
- Tutorials : 18.0
- Laboratory works : -
- Projects : -
- Internship : -
- Written tests : -
ECTS : 3.0
-
Officials : Herve GUIOL
Goals
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.
Content 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
- This lecture is scheduled in "Période(s) Académique(s) 1 and 3" **
PrerequisitesBasic notions in mathematics.
Tests Two partial examinations (E1 & E2).
Continuous assesment : CC
- Lexique et formules
EF: Examen final en période 3
CC1 : contrôle continu de période 1
CC2 : contrôle continu de période 3
RF1 : rendu final de période 1 (distanciel)
RF2 : rendu final de période 3 (distanciel)
O : Oral (présentiel ou distanciel)
# Calcul de la note de chaque période remontée à la scolarité:
- MCC en présentiel **
NP1 = CC1
NP3 = (2EF+CC2)/3- MCC en distanciel **
NP1 = CC1
NP3 = (RF2+CC2)/2
# Calcul des notes des deux bilans:
NB1 = NP1
NB2 = NP3
- Notes
N1 = (NP1+2NP3)/3 # moyenne examen et contrôle continu.
N2 = ((CC1+CC2)/2+2O)/3 # note de rattrapage (la partie du contrôle continu n'est pas rattrapable)
# Calcul des notes pour les jurys session 1 et 2
NFS1 = N1
NR = N2
NFS2 = NR
Additional Information Course ID : 3MM1PA
Course language(s): 
The course is attached to the following structures:
You can find this course among all other courses.
Bibliography Introduction to Probability and Statistics, MIT Open Courseware
G. Grimmett, Probability Theory and Examples. Oxford.
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Date of update October 24, 2023