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Entropy in Probability Dynamical Systems - NMTP569

Title:	Entropie v pravděpodobnostních dynamických systémech
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2019
Semester:	summer
E-Credits:	3
Hours per week, examination:	summer s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech, English
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	Mgr. Michal Kupsa
Teacher(s):	Mgr. Michal Kupsa
Class:	M Mgr. PMSE M Mgr. PMSE > Povinně volitelné
Classification:	Mathematics > Probability and Statistics
Is interchangeable with:	NSTP060

Opinion survey results Examination dates SS schedule Noticeboard

Annotation -

The basic elements of ergodic theory are presented. We mainly focus on entropy and recurrence. Tight relationship between ergodic theory and the theory of finite-states stationary processes will be presented.

Last update: T_KPMS (16.05.2013)

Aim of the course -

To present the broadly used notion of entropy in the frame of the ergodic theory.

Last update: T_KPMS (16.05.2013)

Course completion requirements -

Oral exam.

Last update: Zichová Jitka, RNDr., Dr. (29.10.2019)

Literature - Czech

K. Petersen: Ergodic Theory, Cambridge Univ. Press, 1983

P. Shields: The Ergodic Theory of Discrete Sample Path, Graduate Studies in Mathematics, AMS, 1996

Last update: T_KPMS (16.05.2013)

Teaching methods -

Lecture.

Last update: T_KPMS (16.05.2013)

Requirements to the exam -

According to the sylabus and the content of the lecture.

Last update: Zichová Jitka, RNDr., Dr. (29.10.2019)

Syllabus -

1. Probability (measure-theoretical) dynamical systems, finite-states stationary processes - definitions, examples, ergodicity, isomorfismus of probability dynamical systems, factorization

2. Entropy of the process, entropy of the system, strictly positive entropy - Kolmogorov property

3. Kolmogorov-Sinai theorem on generators, Shannon-McMillan-Breimann theorem

4. Recurrence, Ornstein-Weiss theorem, Lempel-Ziv algorithm for data compression

Last update: T_KPMS (16.05.2013)

Entry requirements -

Basics of mathematical analysis, measure tehory, Lebesgue integral. Basics of probability theory and linear algebra.

Last update: Zichová Jitka, RNDr., Dr. (05.06.2019)