Výuka v letním semestru 2020/2021
Seminář Pravděpodobnostní a statistické problémy (NMSA170) - Úterý 12:20, 14:00
Podmínky získání zápočtu: 5 menších domácích úloh.
Moodle: stránka kurzu.
Cvičení z Prostorového modelování (NMTP438) - Středa 9:00
Podmínky získání zápočtu: přibližně 5 domácích úloh.
Moodle: stránka kurzu.
Teaching in winter semester 2020/2021
Exercise class on Spatial Statistics (NMST543) - Monday 14:00, K10A
We will meet using ZOOM on the scheduled date and time, as indicated in SIS.
Link to the ZOOM meeting, Monday at 14:00, for the first week only (then use the link provided in Moodle): link.
Requirements for obtaining the course credit: regular attendance, a short individual project.
Materials for the exercise classes:
Exercise class on Probability Theory 2 (NMSA405) - Wednesday 10:40, K9; Wednesday 12:20, K9
We will meet using ZOOM on the scheduled date and time, as indicated in SIS.
Requirements for obtaining the course credit: active participation, two homework assignments.
Materials for the exercise classes:
Exercise class on Stochastic Processes 2 (NMSA409) - Monday 15:40, K2
We will meet using ZOOM on the scheduled date and time, as indicated in SIS.
Link to the ZOOM meeting, Monday at 15:40, for the first week only (then use the link provided in Moodle): link.
Requirements for obtaining the course credit are given here. Note that the classes employ a variety of active learning strategies - some background material and reading is available here. Comments on the structure of the course are given here.
Moodle for our course can be found here.
A collection of exercises with solution is available here (version 8.2.2018). English lecture notes are available here.
An example of the test assignment is available here.
Recommended literature:
Prášková, Z.: Základy náhodných procesů II, Karolinum, Praha, 2007.
Brockwell P.J., Davis R.A.: Time series: Theory and Methods, Springer-Verlag, New York, 1987.
Materials for the exercise classes:
Block A, autocovariance function and stationarity.
Block B, L2-properties of stochastic processes.
Block C, spectral decomposition of the autocovariance function.
Block D, linear models of time series.
Block E, ergodicity, prediction.