|
|
|
||
|
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.
Last update: Töpfer Pavel, doc. RNDr., CSc. (26.01.2018)
|
|
||
|
The credit will be given for active participation in tutorials, homeworks and successful completion of tests (the exact weight of each of these criteria is determined by the TA). The nature of the first two requirements does not make it possible for repeated attempts for the credit. The teacher can, however, determine alternative conditions for replacing the missing requirements.
The exam will be written or oral. Obtaining the credit is necessary before the final exam. Last update: Töpfer Pavel, doc. RNDr., CSc. (26.01.2018)
|
|
||
|
D.P. Bertsekas, J.N. Tsitsiklis: Introduction to Probability, Athena Scientific; 2nd edition, 2008. Mor Harchol-Balter: Introduction to Probability for Computing, Cambridge University Press, 2023. G. Grimmett, D. Welsh: Probability - an introduction, Oxford University Press, 2014. M. Mitzenmacher, E. Upfal: Probability and Computing, Cambridge, 2005. R. Bartoszynski, M. Niewiadomska-Budaj: Probability and Statistical Inference, J. Wiley, 1996. K. Zvára, J. Štěpán: Pravděpodobnost a matematická statistika, Matfyzpress, Praha 1997. Last update: Šámal Robert, doc. Mgr., Ph.D. (30.09.2024)
|
|
||
|
(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 Last update: Šámal Robert, doc. Mgr., Ph.D. (03.10.2022)
|