Concept
Exchangeable random variables
Related courses (32)
MATH-232: Probability and statistics (for IC)
A basic course in probability and statistics
BIO-369: Randomness and information in biological data
Biology is becoming more and more a data science, as illustrated by the explosion of available genome sequences. This course aims to show how we can make sense of such data and harness it in order to
CS-101: Advanced information, computation, communication I
Discrete mathematics is a discipline with applications to almost all areas of study. It provides a set of indispensable tools to computer science in particular. This course reviews (familiar) topics a
MATH-442: Statistical theory
-This course gives a mostly rigourous treatment of some statistical methods outside the context of standard likelihood theory.
PHYS-403: Computer simulation of physical systems I
The two main topics covered by this course are classical molecular dynamics and the Monte Carlo method.
COM-102: Advanced information, computation, communication II
Text, sound, and images are examples of information sources stored in our computers and/or communicated over the Internet. How do we measure, compress, and protect the informatin they contain?
MATH-432: Probability theory
The course is based on Durrett's text book
Probability: Theory and Examples.
It takes the measure theory approach to probability theory, wherein expectations are simply abstract integrals.
It takes the measure theory approach to probability theory, wherein expectations are simply abstract integrals.
MATH-413: Statistics for data science
Statistics lies at the foundation of data science, providing a unifying theoretical and methodological backbone for the diverse tasks enountered in this emerging field. This course rigorously develops
MATH-230: Probability
Le cours est une introduction à la théorie des probabilités. Le but sera d'introduire le formalisme moderne (basé sur la notion de mesure), de lier celui-ci à l'aspect "intuitif" des probabilités mais
CS-450: Algorithms II
A first graduate course in algorithms, this course assumes minimal background, but moves rapidly. The objective is to learn the main techniques of algorithm analysis and design, while building a reper