MATS260 Probability Theory 1 (5 cr)
Learning outcomes
The students are familiar with the concept of probability spaces, random variables, and independence.
They are able to describe simple stochastic phenomena within this framework and know important distributions. The notion of expected values along with the main theorems about integration is understood as extension of the Riemann integral.
The students are able to compute expected values based on discrete distributions and the Lebesgue measure on the real line.
Study methods
Course exam and exercises. Part of the exercises may be obligatory.
Final exam is an other option.
Content
Basic concepts of probability:
* probability space
* independence of events
* random variables
* expectation and its basic properties
* independence of random variables
Materials
Lecture notes: C. Geiss and S. Geiss. Introduction to Probability Theory I
Assessment criteria
The grade is based on
a) the number of points in the course exam and possibly additional points from exercises
OR
b) the number of points in the final exam.
At least half of the points are needed to pass the course.
Prerequisites
MATA280 Foundations of stochastics or TILA121 Probability or TILA1200 Probability 1.