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Probability

Some basic information about the module

Cycle of education: 2019/2020

The name of the faculty organization unit: The faculty Mathematics and Applied Physics

The name of the field of study: Mathematics

The area of study: sciences

The profile of studing:

The level of study: first degree study

Type of study: full time

discipline specialities : Applications of Mathematics in Economics

The degree after graduating from university: bachelor's degree

The name of the module department : Departament of Mathematical Modelling

The code of the module: 1066

The module status: mandatory for teaching programme Applications of Mathematics in Economics

The position in the studies teaching programme: sem: 4 / W30 C30 / 6 ECTS / E

The language of the lecture: Polish

The name of the coordinator 1: Mariusz Startek, PhD

office hours of the coordinator: Podane na stronie domowej.

The name of the coordinator 2: Liliana Rybarska-Rusinek, DSc, PhD

The aim of studying and bibliography

The main aim of study: Explore the basic messages and methods of probability.

The general information about the module: Probability, random variable, parameters of random variable, independence, sequences of random variables, limit theorems.

Bibliography required to complete the module
Bibliography used during lectures
1 M. Fisz Rachunek prawdopodobieństwa i statystyka matematyczna PWN, Warszawa. 1969
2 M. Startek Podstawy rachunku prawdopodobieństwa z elementami statystyki matematycznej Oficyna Wydawnicza Politechniki Rzeszowskiej. 2005
3 J. Stankiewicz, K. Wilczek Elementy rachunku prawdopodobieństwa i statystyki matematycznej. Teoria, przykłady, zadania Oficyna Wydawnicza Politechniki Rzeszowskiej. 2000
Bibliography used during classes/laboratories/others
1 M. Startek Podstawy rachunku prawdopodobieństwa z elementami statystyki matematycznej Oficyna Wydawnicza Politechniki Rzeszowskiej. 2005
2 W. Krysicki, J. Bartos, W. Dyczka, K. Królikowska, M. Wasilewski Rachunek prawdopodobieństwa i statystyka matematyczna w zadaniach WNT, Warszawa. 2003
Bibliography to self-study
1 A. i E. Plucińscy Probabilistyka WNT, Warszawa. 2003

Basic requirements in category knowledge/skills/social competences

Formal requirements: The student satisfies the formal requirements set out in the study regulations

Basic requirements in category knowledge: Knowing the analysis.

Basic requirements in category skills: Ability to use basic mathematical apparatus for the analysis.

Basic requirements in category social competences: The student is prepared to take substantially justified mathematical operations in order to solve the posed exercise.

Module outcomes

MEK The student who completed the module Types of classes / teaching methods leading to achieving a given outcome of teaching Methods of verifying every mentioned outcome of teaching Relationships with KEK Relationships with PRK
01 use the concept of probability space, is able construct and analyze the mathematical model of random experiment; lecture, classes test, written exam K_W01+
K_W03+
K_U30++
P6S_UW
P6S_WG
P6S_WK
02 is able to give examples of discrete and continuous probability distributions and discuss selected random experiment and mathematical models in which these distributions ocur; know the practical application of elementary distributions; lecture, classes test, written exam K_W05+
K_U31++
P6S_UK
P6S_UO
P6S_UU
P6S_UW
P6S_WG
03 is able to use the formula of total probability and Bayes formula; lecture, classes test, written exam K_W04+
K_U32++
P6S_UW
P6S_WG
P6S_WK
04 is able to count the parameters of distributions of random variables; is able to use limit theorems and the laws of large numbers. lecture, classes test, written exam K_W02+
K_U33++
K_K01+
P6S_KK
P6S_UW
P6S_WG
P6S_WK

Attention: Depending on the epidemic situation, verification of the achieved learning outcomes specified in the study program, in particular credits and examinations at the end of specific classes, can be implemented remotely (real-time meetings).

The syllabus of the module

Sem. TK The content realized in MEK
4 TK01 Genesis of probability. The sample space. Probability space. Properties of probability. Conditional probability, independence of events. The total probability, Bayes' theorem. Bernoulli trials. One- and n-dimensional random variables. Distribution function of random variable. Examples of discrete and continuous random variables. Correlation and regression. Sequences of random variables. Various types of convergences of random variables. The Tchebychev inequalit. The law of large numbers. The central limit theorem. wykład, ćwiczenia MEK01 MEK02 MEK03 MEK04

The student's effort

The type of classes The work before classes The participation in classes The work after classes
Lecture (sem. 4) contact hours: 30.00 hours/sem.
complementing/reading through notes: 15.00 hours/sem.
Studying the recommended bibliography: 15.00 hours/sem.
Class (sem. 4) The preparation for a Class: 15.00 hours/sem.
The preparation for a test: 10.00 hours/sem.
Others: 5.00 hours/sem.
contact hours: 30.00 hours/sem.
Finishing/Studying tasks: 20.00 hours/sem.
Advice (sem. 4) The participation in Advice: 1.00 hours/sem.
Exam (sem. 4) The preparation for an Exam: 10.00 hours/sem.
The written exam: 2.00 hours/sem.

The way of giving the component module grades and the final grade

The type of classes The way of giving the final grade
Lecture Written exam. The obvious exercises and the extra exercises. The obvious exercises must be solved. Only the obvious exercises - 3.0. Exam only after the credit of classes.
Class Written test. The obvious exercises must be solved. Only the obvious exercises - 3.0.
The final grade After the credit of all types of classes the final grade is the average of grade of classes and grade of exam.

Sample problems

Required during the exam/when receiving the credit
(-)

Realized during classes/laboratories/projects
(-)

Others
(-)

Can a student use any teaching aids during the exam/when receiving the credit : no

The contents of the module are associated with the research profile: yes

1 A. Linkov; E. Rejwer-Kosińska; L. Rybarska-Rusinek The Study of Options for Identification Stress Contrasts via Pumping History 2023
2 A. Linkov; E. Rejwer-Kosińska; L. Rybarska-Rusinek Evaluation of Rockburst Hazard by Accelerated Numerical Modeling of Stressed State and Induced Seismicity 2022
3 A. Linkov; E. Rejwer-Kosińska; L. Rybarska-Rusinek On accuracy of translations by kernel independent fast multipole methods 2022
4 A. Linkov; E. Rejwer-Kosińska; L. Rybarska-Rusinek On evaluation of local fields by fast multipole method employing smooth equivalent/check surfaces 2021
5 A. Linkov; E. Rejwer; L. Rybarska-Rusinek On speeding up nano- and micromechanical calculations for irregular systems with long-range potentials 2020
6 L. Rybarska-Rusinek On evaluation of influence coefficients for edge and intermediate boundary elements in 3D problems involving strong field concentrations 2019
7 L. Rybarska-Rusinek Opracowanie metod i procedur numerycznych do modelowania obszarów o silnej koncentracji pól fizycznych 2019