Probabilities

This course will introduce probability spaces, the concepts of probability and independence, and will define discrete and density random variables with an emphasis on modeling.

Probabilistic spaces.

Random experiments. Events. Parallel between probabilistic vocabulary and set-theoretic vocabulary. Tribes. Probabilities.

ProbabilityConditional probability and independence

Conditional probability; total probability formula; Bayes' formula. Independence of events; Poincaré formula.

Discreterandom variables.

Definition of a random variable. Probability distribution. Distribution function. Moments. Random variable functions. Common discrete distributions: uniform, Bernoulli, binomial, hypergeometric, geometric, Poisson.

Random variables with density:

Distribution function, density. Moments. Random variable functions. Laws defined by a standard density: uniform, exponential, normal.

First-year analysis courses (HAX103X and HAX201X) and  

HAX101X – Reasoning and Set Theory

HAX203X – Arithmetic and Counting

Recommended prerequisites: L1 math

Hourly volumes:

            CM: 24

            TD: 25.5

            TP:

            Land: