De elektronische studiegids voor het academiejaar 2026 - 2027 is onder voorbehoud.





Principles of Statistical Inference (3768)
Coordinating lecturer:Prof. dr. Anneleen VERHASSELT 

Credits: 3,0
Study load hours: 81
Period: semester 1 (3sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes
Sequentiality
No sequentiality


Prerequisites

The student needs to understand and to be able to apply/calculate the basic concepts in probability theory and statistics: random variable, continous/discrete distributions, expectation, variance, multivariate/marginal distribution, independence of random variables, conditional probability/density, central limit theorem, multivariate normal distribution.



Content

This course deals with the theoretical basis of parametric statistical inference. Key concepts of estimation, confidence regions and hypothesis testing are introduced and applied to several parametric statistical models.


Previously purchased compulsory textbooks
  Mathematical Statistics and Data Analysis,John Rice,Brooks/Cole,9780495118688
 

Compulsory course material
 

The R software will be used in this course.

 

Recommended reading
  Statistical Inference,G. Casella; R.L. Berger,second,Brooks/Cole Cengage Learning,9780534243128

An introduction to mathematical statistics,Bijma, Jonker and Van Der Vaart,Amsterdam Univeristy Press,Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/de tail.action?docID=5046611&pq-origsite=summon


Organisational and teaching methods
Organisational methods  
Distance learning ✔  
Response lecture ✔  
Small group session ✔  


Evaluation

Semester 1 (3,00sp)

Evaluation method
Written exam100 %
Closed-book ✔
Open questions✔
Use of study material during evaluation✔
Explanation (English)Formularium is provided.

Second examination period

Evaluation second examination opportunity different from first examination opprt
No


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

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory methodologically, thus contributing to scientific research within the field of statistical and data science.

 
Master of Teaching in Sciences and Technology
  •  EC 

    • 5.4The Educational Master in Science and Technology as a domain expert: the Educational Master has advanced knowledge and understanding of the domain disciplines relevant to the subject didactics.

     

 
Included in these programmesTolerance3
2nd year Master Bioinformatics Y
2nd year Master Bioinformatics - icp Y
2nd year Master Biostatistics Y
2nd year Master Biostatistics - icp Y
2nd year Master Data Science Y
2nd year Master Quantitative Epidemiology Y
2nd year Master Quantitative Epidemiology - icp Y
Exchange Programme Statistics Y
Master of Teaching in Sciences and Technology - Engineering and Technology choice for subject didactics math Y



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