(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload
Ausbildungslevel
Studienfachbereich
VerantwortlicheR
Semesterstunden
Anbietende Uni
4 ECTS
M2 - Master 2. Jahr
Statistik
Andreas Quatember
2 SSt
Johannes Kepler Universität Linz
Detailinformationen
Anmeldevoraussetzungen
(*)keine
Quellcurriculum
Masterstudium Statistics and Data Science 2025W
Lernergebnisse
Kompetenzen
(*)Students are able to apply key concepts of Computational Statistics, as well as independently implement selected methods.
Fertigkeiten
Kenntnisse
(*)
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
Beurteilungskriterien
(*)Exam
Project
Lehrmethoden
(*)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
Abhaltungssprache
Englisch
Literatur
(*)Slides
Supplementary reading will be announced each semester.
Lehrinhalte wechselnd?
Nein
Äquivalenzen
(*)in collaboration with 951STMOSANK14: KV Survival Analysis (4 ECTS) equivalent to 4MSCVDPR: PR Computerintensive Verfahren in der Datenanalyse (6 ECTS)
