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Blekinge Institute of Technology
Department of Mathematics and Natural Science

Revision: 1
Reg.no:


Course syllabus

Introduction to Bayesian Statistics

Introduction to Bayesian Statistics

3 credits (3 högskolepoäng)

Course code: MS2507
Main field of study: The course is not included in any main field of study at BTH
Subject: Mathematics statistics
Disciplinary domain: Natural sciences
Education level: Second-cycle
Specialization: A1N - Second cycle, has only first-cycle course/s as entry requirements

Language of instruction: English
Applies from: 2025-03-11
Approved: 2025-03-11

1. Descision

This course is established by Dean 2024-06-20. The course syllabus is approved by Head of Department of Mathematics and Natural Science 2025-03-11 and applies from 2025-03-11.

2. Entry requirements

Admission to the course requires 5 completed credits in mathematical statistics. English 6.

3. Objective and content

3.1 Objective

This course introduces students to the principles and techniques of Bayesian statistics, also known as Bayesian inference. This powerful framework plays a crucial role in integrating observed data with prior knowledge or external information, enabling more informed decision-making in various applications, particularly in signal processing. By the end of the course, students will have a solid understanding of the foundations of Bayesian methods, setting the stage for further exploration of advanced topics such as artificial intelligence, machine learning, and the analysis of time-dependent systems.

3.2 Content

  • Bayes' theorem
  • Introduction to the Markov Chain Monte Carlo (MCMC)
  • Prior and posterior distributions
  • Model evaluation
  • Bayesian estimation
  • Bayesian networks

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding

On completion of the course, the student will be able to:

  • Demonstrate an understanding of Bayesian methods principles and their practical applications in data analysis.
  • Proactively identify areas where further knowledge or skills are required and take the initiative to acquire them.

4.2. Competence and skills

On completion of the course, the student will be able to:

  • Analyze and synthesize complex statistical data, drawing insights and identifying patterns to inform decision-making.

4.3. Judgement and approach

On completion of the course, the student will be able to:

  • Demonstrate the ability to draw conclusions from statistical data.
  • Show the capability to identify situations where Bayesian statistics is an appropriate approach for problem-solving.
  • Critically evaluate others' choices of statistical methods and the validity of the conclusions drawn.
  • Plan and execute experiments where Bayesian statistics can be applied.

5. Learning activities

The course is delivered through lectures and exercises. Instruction is generally conducted in English. However, instruction in Swedish may be provided if the course instructor deems it necessary.

6. Assessment and grading

Modes of examinations of the course

Code Module Credit Grade
2510 On-campus Examination[1] 2 credits AF
2520 Project 1 credits GU

[1] Determines the final grade for the course, which will only be issued when all components have been approved.

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Failed result, a little more work required, F Fail.

The examiner may carry out oral follow-up of written examinations.

The information before the start of the course states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.

An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.

7. Course evaluation

The course evaluation should be carried out in line with BTH:s course evaluation template and process.

8. Restrictions regarding degree

The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.

9. Course literature and other materials of instruction

Andrew Gelman, John B Carlin, Hal S Stern, David B Dunson, Aki Vehtari (2013) Bayesian Data Analysis, Third edition, Chapman & Hall/CRC (ISBN: 9781439840955)

This is not a legal document. If you would like a copy of the legal decision regarding this course plan, contact the registrar at Blekinge Institute of Technology.