Topics in Advanced Modeling Techniques (3769)
  
Coordinating lecturer :Prof. dr. Geert VERBEKE 
  
Co-lecturer :Prof. dr. Geert MOLENBERGHS 
 Prof. dr. Tomasz BURZYKOWSKI 

Language of instruction : English
Credits: 4,0
  
Period: semester 1 (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
    Generalized Linear Models (5463) 6.0 stptn
    Project: Multivariate and Hierarchical Data (3565) 8.0 stptn
 

Content

Inference for mixed populations.

Non-linear mixed-effects models.

Hidden Markov models.


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


Evaluation

Semester 1 (4,00sp)

Evaluation method
Written exam30 %
Paper✔
Oral exam70 %
Open questions✔
Presentation✔
Evaluation conditions (participation and/or pass)✔
Conditions All components of the evaluation have to be taken up.
Consequences Students get an X score if they do not meet the condition.

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
Explanation (English)The assignment remains the same as for the first chance exam. The
organisation of the exam remains exactly the same.
 

Recommended reading
  Models for Discrete Longitudinal Data,Molenberghs, Geert; Verbeke, Geert,1,Springer-Verlag New York,9780387251448,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=3027 79&pq-origsite=summon


Learning outcomes
Master of Statistics and Data Science
  •  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 oral communicator in their own field.

     
  •  DC 

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

     
  •  DC 

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

     
  •  DC 

    • The student is an effective writer, both within their own field as well as across disciplines.

  •  EC 

    • The student is capable of acquiring new knowledge.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
2nd year Master Bioinformatics J
2nd year Master Biostatistics N
2nd year Master Biostatistics - icp N
2nd year Master Data Science J
Exchange Programme Statistics 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.