Probability Theory
Course code
MLM6508.DT
old course code
Course title in Estonian
Tõenäosusteooria
Course title in English
Probability Theory
ECTS credits
6.0
Assessment form
Examination
lecturer of 2025/2026 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
lecturer of 2026/2027 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
The aim of this course is to equip students with the fundamental concepts and methods of probability theory (rules for event probabilities, combinatorics, conditional probability, discrete and continuous distributions) and to develop their ability to apply them in modeling stochastic processes and assessing event probabilities.
Brief description of the course
Basic concepts of probability theory. Computation of classical probability. Relative frequency of an event. Statistical probability. Geometric probability. Probability of the union and intersection of events. Use of combinatorics in probability calculations. Conditional probability, the law of total probability and Bayes’ theorem. Bernoulli’s formula. Generating functions and their applications. Expectation (mean) and standard deviation of a discrete random variable. Normal distribution. Uniform distribution. Binomial distribution. Normal approximation to the binomial distribution. Geometric distribution. Poisson distribution.
Learning outcomes in the course
Upon completing the course the student:
- explains and applies main laws and concepts of probability (classical probability, statistical probability, geometric probability; operations with events; conditional probability; formulas of total probability and Bayes);
- uses combinatorics and understands how to use main discrete distributions (Bernoulli distribution, binomial distribution, geometric distribution and Poisson distribution) for calculating the probabilities of events and characteristics of random variables;
- describes and uses main continuous distributions (uniform distribution and normal distribution) and applies normal distribution to approximate binomial distribution;
- creates and interprets probabilistic models describing different problems.
- explains and applies main laws and concepts of probability (classical probability, statistical probability, geometric probability; operations with events; conditional probability; formulas of total probability and Bayes);
- uses combinatorics and understands how to use main discrete distributions (Bernoulli distribution, binomial distribution, geometric distribution and Poisson distribution) for calculating the probabilities of events and characteristics of random variables;
- describes and uses main continuous distributions (uniform distribution and normal distribution) and applies normal distribution to approximate binomial distribution;
- creates and interprets probabilistic models describing different problems.
Teacher
Tõnu Tõnso
Prerequisite course 1
The course is a prerequisite
Study programmes containing that course