Aims The aim of the course is to enable students to understand and use important techniques in Computational Statistics. The students will study computational methods used in Statistics with emphasis on large-scale computations and will develop understanding and skills how to use these appropriately in research and applications.

Syllabus/Course outline Numerical linear algebra theory and methods, suited for statistical computing, linear regression models, data mining, pattern recognition, principal component analysis, Markov Chain Monte Carlo methods, Total Least Squares, Partial Least Squares.

Synopsis The course discusses selected topics in Computational Statistics, and promotes the understanding of the crucial role of numerical computation in modern statistical research and application of statistical methods to real-life problems. The focus is on solving computationally intensive tasks, enabled by the availability of powerful computer facilities.

Target group The course is intended for students and researchers in various fields where Statistics is used as a modelling and analysis tool.