Syllabus
(The course will be in English if there is somebody signed up who does not understand Czech.)
Markov chains: basic concept and basic use probabilistic algorithm for 2-SAT, 3-SAT stationary distribution and the convergence to it.
Model balls-into-bins: use for analysis of hashing, Poisson approximation, estimates.
Poisson's process
Moment generating functions and the proof of Central Limit Theorem.
Conditional expectation. Coupling.
Bayesian statistics
Fundamentals of Information Theory
Graphical models, belief propagation
Annotation
More advanced parts of probability and statistics for students of computer science. It will be assumed that the students understand material covered by Probability and Statistics 1.