Introduction to Bayesian Inference DL (3579)
Coordinating lecturer:Prof. dr. Christel FAES 
Member of the teaching team:Prof. dr. Ivy JANSEN 
 Dhr. Paulo BATIDOR 

Credits: 4,0
Study load hours: 108
Period: semester 2 (4sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes
Sequentiality
Mandatory sequentiality bound on the level of programme components
 
 
Group 1
 
  Following programme components must have been included in your study programme in a previous education period
    Concepts of Probability and Statistics DL (3220) 5.0 stptn  
 
Or group 2
 
  Following programme components must have been included in your study programme in a previous education period
    Concepts of Probability and Statistics DL (9843) 6.0 stptn  
 


Prerequisites

The student has knowledge of basic concepts from probability, statistics and distributions, as well as basic R programming and reporting skills.

The student has knowledge of statistical inference.


Content

This course will give a broad introduction to basic concepts of Bayesian analysis. Posterior summary measures, predictive distributions and Bayesian hypothesis tests will be contrasted with the frequentist approach. Simulation methods such as
Markov chain Monte Carlo (MCMC) enable the Bayesian analysis. An introduction to algorithms like Gibbs sampling and Metropolis-Hastings will be explained and illustrated. Various medical case studies will be considered.
The student should be able to analyse relatively simple problems in a Bayesian way using BUGS language (e.g. JAGS software). The emphasis in this course is on theoretical background of basic concepts and practical data analysis.


Compulsory textbooks (bookshop)
 

Textbook 1:

Bayesian Biostatistics, Lesaffre, E. and Lawson, A., 2012, John Wiley & Sons

ISBN: 9780470018231
 

Compulsory course material
 

All course material (handouts, video lectures and exercises) will be available on Blackboard.

The software R is used in this course. 


Organisational and teaching methods
Organisational methods  
Collective feedback moment ✔  
Distance learning ✔  
Project ✔  


Evaluation

Semester 2 (4,00sp)

Evaluation method
Written evaluation during teaching period30 %
Transfer of partial marks within the academic yearYes, with condition
Conditions transfer of partial marks within the academic yearscore greater or equal to 10/20 on project
Written exam70 %
Closed-book ✔
Multiple-choice questions✔
Off campus online evaluation/exam✔
For the full evaluation/exam✔
Explanation (English)Yes
Use of study material during evaluation✔
Explanation (English)During the exam, you may bring a summary of the course notes (maximum 5 A4 sheets - double-sided) and a calculator. No other material is allowed.
Evaluation conditions (participation and/or pass)✔
Conditions

The student must submit the project in time.
Consequences

If the student does not submit the project in time, then the final course of this course will receive code "N" (not all evaluation components completed).
Additional information At the exam, a summary of the course notes (5 A4 pages – two-sided) can be used. No other course material is allowed.

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
Explanation (English)Score for project is carried over to the retake exam (if score greater
or equal to 10/20). Otherwise, a new project assignment has to be made.
Score for the written exam cannot be carried over to the second chance
exam.


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

     
  •  DC 

    • ... correctly using state-of-the-art analysis methodology.

     
  •  DC 

    • ... correctly using state-of-the-art software.

  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective writer in their own field.

  •  EC 

    • The student is capable of acquiring new knowledge.

 
Included in these programmesTolerance3
1st year Master Bioinformatics - distance learning N
1st year Master Biostatistics - distance learning N
1st year Master Data Science - distance learning N
1st year Master Quantitative Epidemiology - distance learning N
1st year Master Statistics - Bioinformatics DL basis compulsory Y
1st year Master Statistics - Biostatistics DL basis compulsory Y
1st year Master Statistics - Data Science DL basis compulsory Y
1st year Master Statistics - Epidemiology DL basis compulsory Y



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.