University of Bialystok - Central Authentication System

Probability Theory

General data

Course ID:	360-MS2-1PRBa
Erasmus code / ISCED:	11.103 The subject classification code consists of three to five digits, where the first three represent the classification of the discipline according to the Discipline code list applicable to the Socrates/Erasmus program, the fourth (usually 0) - possible further specification of discipline information, the fifth - the degree of subject determined based on the year of study for which the subject is intended. / (0541) Mathematics The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Probability Theory
Name in Polish:	Probability Theory
Organizational unit:	Faculty of Mathematics
Course groups:	(in Polish) Erasmus+ sem. zimowy
ECTS credit allocation (and other scores):	6.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	elective courses
Prerequisites (description):	Mathematical Analysis III Combinatorics
Mode:	Blended learning
Short description:	Field of science: natural science; discipline: mathematics We will discuss the mathematical formulation of basic probability theory, including random variables and the laws of large numbers. The limitations of the theory will motivate us to study measure-theoretic probability theory.
Full description:	Course profile: academic Form of study: stationary Course type: obligatory Field of science: natural science; Academic discipline: Mathematics Year: 2, semester: 3 Prerequisities: Mathematical Analysis III; Combinatorics lecture 30 h. exercise class 30 h. Verification methods: lectures, exercises, consultations, studying literature, home works, discussions in groups. ECTS credits: 6 Balance of student workload: attending lectures15x2h = 30h attending exercise classes 7x4h + 2h(preliminary teaching) = 30h preparation for classes 7x3h = 21h completing notes after exercises and lectures 7x2h = 14h consultations 5x2h = 10h the final examination: preparation.and take 15h + 4h = 19h Quantitative description Direct interaction with the teacher: 75 h., 3 ECTS Practical exercises: 75 h., 3 ECTS
Learning outcomes:	Has general knowledge of classical probabilistic problems, including the laws of large numbers and limit theorems for discrete random variables.KA6_WG03, KA6_WG07 Knows the concept and basic properties of probability.KA6_WG03, KA6_WG04, KA6_WG07, KA6_UW19 Knows basic probability calculation schemes, including Bernoulli's scheme.KA6_WG03, KA6_WG04, KA6_WG07, KA6_UO01 Is able to give examples of discrete and continuous probability distributions and discuss selected random experiments and the mathematical models in which these distributions occur.KA6_WG03, KA6_WG04, KA6_WG07, KA6_WG02, KA6_UW20, KA6_UO01, KA6_KO01 Is able to determine the basic parameters of the distribution of a random variable with a discrete and continuous distribution. KA6_WG03, KA6_WG04, KA6_WG07, KA6_WG02, KA6_UW21, KA6_UO01, KA6_KO01 Is able to build a probabilistic model for a given random event and indicate the method of calculating the probability. KA6_WG03, KA6_WG04, KA6_WG07, KA6_WG02, KA6_UW19, KA6_UO01, KA6_KO01 Is able to use the basic schemes of probability calculus, including the total probability formula and Bayes' formula.KA6_WG03, KA6_WG04,KA6_WG02, KA6_UO01, KA6_KO01 Is able to describe discrete random phenomena in the world around him, with the appropriate use of language and probabilistic concepts. KA6_WG02, KA6_UW19, KA6_UO01, KA6_KO01 Knows the limitations of one's own knowledge and understands the need for further education in the field of probability theory. KA6_KK01, KA6_KO01
Assessment methods and assessment criteria:	Examinations Graded exercises

Classes in period "Academic year 2023/2024" (past)

Time span:	2023-10-01 - 2024-06-30	Selected timetable range: weekly course term Go to timetable MO TU W TH WYK CW FR
Type of class:	Class, 30 hours more information Lecture, 30 hours more information
Coordinators:	Tomasz Czyżycki, Andrew McKee, Aneta Sliżewska
Group instructors:	Andrew McKee
Students list:	(inaccessible to you)
Credit:	Course - Examination Class - Grading
Bibliography:	R Meester, A natural introduction to probability theory. Birkhäuser, 2008. Available online at https://link.springer.com/book/10.1007/978-3-0348-7786-2 (on campus). A N Shiryaev, Probability (second edition). Graduate Texts in Mathematics, volume 95. Springer, 1996. Available online at https://link.springer.com/book/10.1007/978-1-4757-2539-1 (on campus). T Cacoullos, Exercises in probability. Problem Books in Mathematics. Springer-Verlag, 1989. Available online at https://link.springer.com/book/10.1007/978-1-4612-4526-1 (on campus).

Classes in period "Academic year 2024/2025" (past)

Time span:	2024-10-01 - 2025-06-30	Selected timetable range: weekly course term Go to timetable
Type of class:	Class, 30 hours more information Lecture, 30 hours more information
Coordinators:	Andrew McKee
Group instructors:	Andrew McKee
Students list:	(inaccessible to you)
Credit:	Course - Examination Class - Grading

Course descriptions are protected by copyright.
Copyright by University of Bialystok.