Probability Calculus
Description: The course includes the study of elements of probability theory: probability spaces, random events, axioms of probability, conditional probability, independence of events, random variables, distribution functions, mathematical expectation, variance, correlation, density functions, laws of large numbers and the central limit theorem. Practical applications in the field of statistics, engineering and IT are considered.
Amount of credits: 3
Course Workload:
| Types of classes | hours |
|---|---|
| Lectures | 15 |
| Practical works | 15 |
| Laboratory works | |
| SAWTG (Student Autonomous Work under Teacher Guidance) | 15 |
| SAW (Student autonomous work) | 45 |
| Form of final control | Exam |
| Final assessment method |
Component: Component by selection
Cycle: Base disciplines
Goal
- Mastering the basic concepts and methods of probability theory for modeling random phenomena and uncertainty analysis in applied problems.
Learning outcome: knowledge and understanding
- understand and apply the basic concepts of probability theory;
- be able to calculate the probabilities of events and characteristics of random variables;
Learning outcome: applying knowledge and understanding
- use standard distributions to model random processes;
- apply probability-theoretical methods for uncertainty analysis;
Learning outcome: formation of judgments
- The student is able to interpret probabilistic models in relation to real situations, analyze the degree of uncertainty and make informed decisions based on probabilistic estimates.
Learning outcome: communicative abilities
- The student is able to explain the meaning of probabilistic concepts and results of analyses to both specialists and non-specialists using clear and correct language, and to participate in the discussion of probabilistic models in an interdisciplinary environment.
Learning outcome: learning skills or learning abilities
- The student demonstrates readiness for further in-depth study of probability theory and related disciplines (mathematical statistics, stochastic processes), as well as for independent mastery of new approaches and packages of applied statistics.
