FY823: Bayesian inference and information theory
Study Board of Science
Teaching language: Danish, but English if international students are enrolled
EKA: N510025112, N510025102
Assessment: Second examiner: None
Grading: Pass/Fail
Offered in: Odense
Offered in: Spring
Level: Master
STADS ID (UVA): N510025101
ECTS value: 5
Date of Approval: 24-10-2018
Duration: 1 semester
Version: Archive
Comment
07013201 (former UVA) is identical with this course description.
If there are fewer than 12 students enrolled, the course may. be held with another teaching form.
If there are fewer than 12 students enrolled, the course may. be held with another teaching form.
Entry requirements
Academic preconditions
Students taking the course are expected to: Have knowledge of probability theory, (f.ex. conditional probabilities).
Course introduction
NOTE that the teaching method in the course is special – see the elaboration on the teaching method below.
The aim of the course is to give the students knowledge of the Bayesian way of thinking and solving inference problems as well as the thoughts behind information theory (Bayesian inference is a method where regular probability theory is extended to apply to hypothesis, whereby it constitutes an alternative to frequentist statistics. Information theory is about efficient storage and communication of data).
The course gives a foundation for applying Bayesian methods to analyse data, as for instance obtained through experiments, simulations or registration. This can for instance be used in projects in the rest of the education. The course also gives knowledge of the connection between information theory and the concept of entropy in statistical physics.
In relation to the competence profile of the degree it is the explicit focus of the course to:
- give skills to analyse data and evaluate the plausibility of different physical theories.
- give knowledge and understanding of Bayesian inference and information theory.
- give ability to acquire new knowledge in an effective and autonomous way and communicate this knowledge to colleagues
Expected learning outcome
The learning objective of the course is that the student demonstrates the ability to:
- Autonomously acquire the knowledge about Bayesian inference and information theory as described under Contents
- Solve problems regarding fitting of parameters and model selection
- Present theory and exercises
- Apply the acquired knowledge within a chosen subject (final project)
Content
The following main topics are contained in the course:
- Interpretation of probability
- Model selection
- Fitting of parameters
- Bayes’ theorem
- Shannon entropy
- Coding theory
Literature
Examination regulations
Prerequisites for participating in the exam a)
Timing
Spring
Tests
Active participation in the teaching
EKA
N510025112
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
0
Additional information
The prerequisite examination is a prerequisite for participation in exam element a).
First and foremost a number of presentations around the level of the total number divided by the number of students as well as posing questions during other students’ presentations. Besides that, some hand-ins.
First and foremost a number of presentations around the level of the total number divided by the number of students as well as posing questions during other students’ presentations. Besides that, some hand-ins.
Exam element a)
Timing
June
Prerequisites
| Type | Prerequisite name | Prerequisite course |
|---|---|---|
| Examination part | Prerequisites for participating in the exam a) | N510025101, FY823: Bayesian inference and information theory |
Tests
Oral exam
EKA
N510025102
Assessment
Second examiner: None
Grading
Pass/Fail
Identification
Student Identification Card
Language
Normally, the same as teaching language
Examination aids
To be announced during the course
ECTS value
5
Additional information
The exam takes its starting point in a written report about the final project. It takes the form of a presentation followed by questions.
The examination form for re-examination may be different from the exam form at the regular exam.
The examination form for re-examination may be different from the exam form at the regular exam.
Indicative number of lessons
Teaching Method
At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.
The teaching method in the course is built around the students presenting the material for each other. At every class (2 hours) there will be typically 5-6 students who each spend approximately 10-15 min. on presenting a section or exercise from the textbook. The teachers will be available in between the classes if the presenters have questions to their material or exercise. Due to the additional time needed for preparing the presentations then the number of classes is lower than usual. If less than 12 students has signed up for the course then the number of teaching hours could be reduced to 23 with supplementary group work instead.
Activities during the study phase:
- Study of the textbook
- Solving of exercises
- Preparation of presentations
- Undertaking of project