Introduction to Bayesian Inference DL (3579)
  
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 DL (3220) 5.0 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 OpenBugs 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 ✔  
Project ✔  
Response lecture ✔  


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 ✔
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.

The software R is used 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.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
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



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.