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[ 951STCOCSTK14 ] KV Computational Statistics
Versionsauswahl
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)
On-site course
Maximum number of participants 20
Assignment procedure Assignment according to priority