732A40 Probability Theory
The course aims to provide the student with a solid understanding of basic results and methods in probability. The topics covered include: major classes of probability distributions, multivariate random variables, conditioning, transforms, order statistics, multivariate normal distributions, and convergence concepts.
The teaching is comprised of lectures and
The lectures are devoted to presentations of theories, concepts, and methods.
The seminars are devoted to presentation and discussion of assignments
Examination:A written final exam. See further under exam.
Gut, A. An intermediate course in probability. 2nd
ed. Springer-Verlag, New York, 2009. ISBN 978-1-4419-0161-3
Misprints and corrections may be read at http://www2.math.uu.se/~allan/81misprints.pdf
The first lecture will be held Monday August 24, 2015 at 13.15 in BL33
The time before the first lecture should be spent on self-study to fresh up your knowledge in probability theory from previous course(s). Before the first lecture, students should be familiar with at least the following concepts:
- Probabilities of events
- The basic laws of probabilities
- Bayes theorem for events
- Random variables
- Probability distributions
- The most common distributions: normal, chi-square, student t, binomial and poisson.
- Expected value and variance
- Covariance and correlation
- Linear regression
Basic skills in probability theory corresponding to an
introductory course in statistics.
Calculus (corresponding to a first course)
- The course material is maintained on GitHub. Here is the repository for the course.
- Table with common integrals
- Exams with solutions from a course at KTH. But note that not all exam questions are relevant for this course, in particular the ones for stochastic processes (which we do not cover in this course).
- Wikipedia's page on complex numbers is quite good and compact.
- CRAN list of distributions in R.
Page responsible: Per Sidén
Last updated: 2015-06-22