MATS262 Probability Theory 2 (5 cr)
Learning outcomes
After the course
* the student knows the types of convergence of random variables and measures as well as their relations to each other,
* the student is familiar with the behaviour of sums of independent random variables, and knows the Law of large numbers and the Central limit theorem
* the student can identify (multidimensional) Gaussian distributions and describe their properties using characteristic functions
Study methods
Course exam and exercises. Part of the exercises may be obligatory.
Final exam is an other option.
Content
* Types of convergence of random variables and measures
* Sums of independent random variables
* Convolution of probability measures
* Law of large numbers
* Central limit theorem
Materials
Lecture notes: C. Geiss and S. Geiss: An Introduction to Probability Theory.
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
MATS260 Probability theory 1