Introduction of Bayesian Inference (3562)
  
Coordinating lecturer :Prof. dr. Christel FAES 
  
Member of the teaching team :dr. Bryan SUMALINAB 
 Prof. dr. Ivy JANSEN 
 dr. Minh Hanh NGUYEN 
 De heer Pieter GIESEN 

Language of instruction : English
Credits: 4,0
  
Period: semester 2 (4sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   Mandatory sequentiality bound on the level of programme components
 
 
  Following programme components must have been included in your study programme in a previous education period
    Concepts of Probability and Statistics (1798) 5.0 stptn
 

Prerequisites

The students 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 OpenBugs, Nimble or JAGS software. The emphasis in this course is on theoretical background of basic concepts and practical data analysis.


Organisational and teaching methods
Organisational methods  
Distance learning ✔  
Lecture ✔  
Project ✔  


Evaluation

Semester 2 (4,00sp)

Evaluation method
Written evaluation during teaching period30 %
Transfer of partial marks within the academic year✔
Conditions transfer of partial marks within the academic yearscore greater or equal to 10/20 on project
Written exam70 %
Closed-book ✔
Multiple-choice questions✔
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.
 

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.

R will be used as software in this course. 


Learning outcomes
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.

 
Master of Business and Information Systems Engineering
  •  EC 

    • EC 08: The holder of the degree shows autonomy in implementing scientific research methods. (Research skills)

  •  EC 

    • EC 09: The holder of the degree shows autonomy in analysing, interpreting, evaluating and reporting research results.  (Research skills)

  •  EC 

    • EC 16: The holder of the degree uses data science and IT to design decision support systems that provide useful insights with which the quality of decisions can be improved. (Programme-specific competencies)

 
Master of Teaching in Sciences and Technology
  •  EC 

    • 5.4. The master of education is a domain expert SCIENCES: the EM has advanced knowledge and understanding of the domain disciplines relevant to the specific subject doctrine(s).

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
1st year Master Bioinformatics N
1st year Master Bioinformatics - icp N
1st year Master Biostatistics N
1st year Master Biostatistics - icp N
1st year Master Data Science N
1st year Master Quantitative Epidemiology - icp N
1st year Quantitative Epidemiology N
2nd Master of Business and Information Systems Engineering J
Exchange Programme Mathematics J
Exchange Programme Statistics J
Master of Teaching in Sciences and Technology - Engineering and Technology choice for subject didactics math J



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.