Es ist eine neuere Version 2026W dieser LV im Curriculum Master's programme Computational Mathematics 2026W vorhanden.
Workload
Education level
Study areas
Responsible person
Hours per week
Coordinating university
4 ECTS
M2 - Master's programme 2. year
Statistics
Andreas Quatember
2 hpw
Johannes Kepler University Linz
Detailed information
Pre-requisites
keine
Original study plan
Master's programme Statistics and Data Science 2025W
Learning Outcomes
Competences
Students are able to apply key concepts of Computational Statistics, as well as independently implement selected methods.
Skills
Knowledge
Knowing how important statistical methods are implemented in statistical software (k1,k2)
Knowing about key principles of efficient and accurate numerical computation (k1, k2)
Implementing algorithms commonly used in computational statistics (k4)
Computer arithmetic
Methods of non-linear optimization and root finding
Application of optimization to obtain maximum likelihood estimates and confidence intervals
Numerical implementation of linear and generalized linear models
Computational Aspects of Mixed Effects Models
EM algorithm
Bayesian and approximate Bayesian computation
Random Number Generation
Criteria for evaluation
Exam
Project
Methods
Lecture by instructor; Discussion of the projects, where the solution is presented by the students in a project report; Independent development and application of computational statistical methods
Language
English
Study material
Slides
Supplementary reading will be announced each semester.
Changing subject?
No
Corresponding lecture
in collaboration with 951STMOSANK14: KV Survival Analysis (4 ECTS) equivalent to 4MSCVDPR: PR Computerintensive Verfahren in der Datenanalyse (6 ECTS)
