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Statistical thinking in financial mathematics - NMFM261

Title:	Statistické myšlení ve finanční matematice
Form of teaching:	seminar
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2025
Duration in semesters:	1
Semester:	winter
E-Credits:	2
Hours per week, examination:	winter s.:0/2, C [HT]
Capacity:	unlimited
Maximum number of enrolled students:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Repeated enrollment:	2 / 2 / 2 / 2

Guarantor:	prof. RNDr. Ivan Mizera, CSc.
Teacher(s):	prof. RNDr. Ivan Mizera, CSc.
Class:	M Bc. FM M Bc. FM > Doporučené volitelné M Bc. FM > 2. ročník
Classification:	Mathematics > Probability and Statistics
Incompatibility :	NMSA260
Is incompatible with:	NMSA260

Opinion survey results WS schedule Noticeboard

Annotation -

Principles of statistical thought in obtaining conclusions under uncertainty will be exposed on selected real examples of decision, learning, and prediction problems.

Last update: Omelka Marek, doc. Ing., Ph.D. (28.04.2025)

Aim of the course -

The objective of the course is to demonstrate how uncertainty can be effectively handled in the economic, financial, and insurance practice.

Last update: Mizera Ivan, prof. RNDr., CSc. (05.09.2026)

Course completion requirements -

Active participation in classes (max. 3 absences) and a short project (details in class).

Last update: Mizera Ivan, prof. RNDr., CSc. (31.08.2026)

Literature - Czech

Josef Ježek: Příručka kupecké, finanční a pojistné aritmetiky. Praha, 1948

Last update: Mizera Ivan, prof. RNDr., CSc. (05.09.2026)

Teaching methods -

Seminar.

Last update: Omelka Marek, doc. Ing., Ph.D. (28.04.2025)

Syllabus -

1. Basic concepts of probability and statistics: random variable and its distribution, Bayes theorem, correlation

2. Linear regression, contingency tables

3. Data visualization

4. Paradoxes and classical statistical problems: e.g. Von Neumann’s unfair coin, voting paradoxes, German tank problem

5. Practical examples of application and correct interpretation of statistical models from disciplines including medicine, industrial production, sport, criminology, education, etc.

Last update: Omelka Marek, doc. Ing., Ph.D. (28.04.2025)