Entry requirements

Analysis MN2.

Aims

Basic knowledge of probability theory.

Content

Combinatorics, the probability concept,

computations with probabilities, random

variables, some common probability distributions,

independence and conditioning, expected value

and variance, moment generating functions, the central limit theorem, the law of

large numbers, the Poisson process, simple random

walk, simulation. Practical examples on the

construction of probability models.

Instruction

Lectures, problem solving sessions and

laboratory sessions.

Assessment

Written examination at the end of the course.

Written exercises and assignments

may be used during the course.