Bayesian Learning, 6 credits
Bayesian Learning, 6 hp
732A73
Main field of study
StatisticsCourse level
Second cycleCourse type
Single subject and programme courseCourse coordinator
Mattias VillaniDirector of studies or equivalent
Ann-Charlotte HallbergAvailable for exchange students
YesContact
Isak Hietala
Course offered for | Semester | Weeks | Timetable module | Language | Campus | ECV | |
---|---|---|---|---|---|---|---|
Single subject course (Half-time, Day-time) | Spring 2020 | 202014-202023 | 2 | English | Linköping, Valla | ||
Single subject course (Half-time, Day-time) | Spring 2020 | 202014-202023 | 2 | English | Linköping, Valla |
Main field of study
StatisticsCourse level
Second cycleAdvancement level
A1XEntry requirements
- Bachelor’s degree equivalent to a Swedish Kandidatexamen of 180 ECTS credits including an in-depth academic paper 15 ECTS credits in one of the following subjects:
Statistics
Mathematics
Applied mathematics
Computer science
Engineering
or equivalent - Passed courses in:
Calculus
Linear algebra
Statistics
Programming - Passed intermediate course in probability and statistical inference
- Passed course including multiple linear regression
- English corresponding to the level of English in Swedish upper secondary education
(Engelska 6/B)
(Exemption from Swedish)
Intended learning outcomes
After completion of the course the student should at an advanced level be able to:
- account for the main differences between Bayesian and frequentist inference,
- analyze basic statistical models using a Bayesian approach and correctly interpret the results,
- use Bayesian models for prediction and decision making,
- implement more advanced statistical models using modern simulation methods,
- perform Bayesian model inference.
Course content
The course covers the following topics:
Likelihood, Subjective probability, Bayes theorem, Prior and posterior distribution, Bayesian analysis of the following models: Bernoulli, Normal, Multinomial, Multivariate normal; Linear and nonlinear regression, Binary regression, Mixture models; Regularization priors, Classification, Naïve Bayes, Marginalization, Posterior approximation, Prediction, Decision theory, Markov Chain Monte Carlo, Gibbs sampling, Bayesian variable selection, Model selection, Model averaging.
Teaching and working methods
The teaching comprises lectures, exercise sessions, and computer labs. The lectures are devoted to presentations of concepts and methods. Mathematically oriented problems are solved in the exercise sessions. The computer labs are used for practical applications of Bayesian inference. Homework and independent study are a necessary complement to the course.
Language of instruction: English.
Examination
Written reports on computer lab assignments, and a computer exam. Detailed information about the examination can be found in the course’s study guide.
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 carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.
Department
Institutionen för datavetenskapCode | Name | Scope | Grading scale |
---|---|---|---|
LAB1 | Laboratory work | 3 credits | EC |
TENT | Examination | 3 credits | EC |
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