732A63 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. Active participation in the seminars gives bonus points to the exam. See further under examination.
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 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)
- Table with common integrals
- Table with common mathematical and statistical formulas
- 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.
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Last updated: 2020-12-22