MTTTS12 Introduction to Bayesian Analysis 1 5 ECTS
Organised by
Degree Programme in Mathematics and Statistics
Corresponding course units in the curriculum
School of Information Sciences
Curricula 2015 – 2017

Learning outcomes

After the course, the student will be able to determine posterior distributions, Bayes estimates, posterior intervals and posterior predictive distributions in some simple cases, and do Bayesian hypothesis testing. Further, he will be able to analyse single-parameter and simple multiparameter models using software such as BUGS.

Contents

Prior and posterior distributions, Bayes estimators, posterior predictive distribution, interval estimation and hypothesis testing, single-parameter models, simple multiparameter models.

Modes of study

Option 1
Available for:
  • Degree Programme Students
  • Other Students
  • Open University Students
  • Doctoral Students
  • Exchange Students
Participation in course work 
In English
Option 2
Available for:
  • Degree Programme Students
  • Other Students
  • Open University Students
  • Doctoral Students
  • Exchange Students
Written exam 
In English

Evaluation

Numeric 1-5.

Belongs to following study modules

Faculty of Natural Sciences
2017–2018
Teaching
Faculty of Natural Sciences