Probability Theory, 6 credits
Sannolikhetsteori, 6 hp
732A63
Main field of study
StatisticsCourse level
Second cycleCourse type
Programme courseExaminer
Johan AlenlövCourse coordinator
Johan AlenlövDirector of studies or equivalent
Jolanta PielaszkiewiczCourse offered for | Semester | Weeks | Language | Campus | ECV | |
---|---|---|---|---|---|---|
Single subject course (, ) | Autumn 2025 | |||||
F7MML | Statistics and Machine Learning, Master´s Programme - First and main admission round | 3 (Autumn 2025) | E | |||
F7MML | Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students) | 3 (Autumn 2025) | E |
Main field of study
StatisticsCourse level
Second cycleAdvancement level
A1FCourse offered for
- Master's Programme in Statistics and Machine Learning
Entry requirements
- Bachelor's degree equivalent to a Swedish Kandidatexamen of 180 ECTS credits in one of the following subjects:
- statistics
- mathematics
- applied mathematics
- computer science
- engineering
- Completed courses in
- calculus
- linear algebra
- statistics
- programming
- English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
Exemption from Swedish - At least 24 ECTS credits passed in the main field of Statistics at second cycle and at least 5 ECTS credits passed in the main field of Computer Science at second cycle
Intended learning outcomes
After competition of the course, the students shall be able to:
- use the major univariate and multivariate probability distributions in solving theoretical and practical problems in probability
- derive probability distributions of functions of random vectors
- analyze probability models by moment generating functions and other transforms
- analyze probability models by conditioning
- account for basic modes of stochastic convergence and derive limit distributions.
Course content
The course provides a theoretical foundation for models and methods based on the concept of probability. The course comprises:
- probability distributions for univariate and multivariate random variables,
- expected value, variance, moments,
- joint distribution, conditional distribution, independence,
- the elements of the Bayesian approach,
- transforms,
- order statistics,
- multivariate normal distribution and its properties,
- types of convergence and convergence theorems.
Teaching and working methods
The course consists of lectures and exercise sessions. The lectures are devoted to presentations of theories, concepts and methods. Mathematically oriented problems are solved in the exercise sessions.
Homework and independent study are a necessary complement to the course. Language of instruction: English.
Examination
Written examination.
Detailed information about the examination can be found in the course’s study guide.
If special circumstances prevail, and if it is possible with consideration of the nature of the compulsory component, the examiner may decide to replace the compulsory component with another equivalent component.
If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it.
If the coordinator has recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.
An examiner may also decide that an adapted examination or alternative form of examination if the examiner assessed that special circumstances prevail, and the examiner assesses that it is possible while maintaining the objectives of the course.
Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.
Students who have passed an examination may not retake it in order to improve their grades.
Grades
ECTS, ECOther information
Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus.
The course is conducted in such a way that there are equal opportunities with regard to sex, transgender identity or expression, ethnicity, religion or other belief, disability, sexual orientation and age.
If special circumstances prevail, the vice-chancellor may in a special decision specify the preconditions for temporary deviations from this course syllabus, and delegate the right to take such decisions.
About teaching and examination language
The teaching language is presented in the Overview tab for each course. The examination language relates to the teaching language as follows:
- If teaching language is “Swedish”, the course as a whole could be given in Swedish, or partly, or as a whole, in English. Examination language is Swedish, but parts of the examination can be in English.
- If teaching language is “English”, the course as a whole is taught in English. Examination language is English.
- If teaching language is “Swedish/English”, the course as a whole will be taught in English if students without prior knowledge of the Swedish language participate. Examination language is Swedish or English depending on teaching language.
Department
Institutionen för datavetenskapCode | Name | Scope | Grading scale |
---|---|---|---|
KTR1 | Examination | 0 credits | D |
TENT | Examination | 6 credits | EC |
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