Institute of Information Theory and Automation

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Department of Stochastic Informatics

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Research in the department is concentrated on following fields of mathematics:

  • Stochastic analysis, with emphasis on infinite-dimensional problems and interacting particle systems
  • Strictly stationary processes and ergodic theory
  • Analysis of statistical data, with focus on multidimensional nonparametric statistics and survival analysis
  • Statistical signal processing, in particular blind signal separation problems
2017-02-03 11:50

Department detail

Mgr. Pavel Boček
Mgr. Lucie Fajfrová Ph.D.
Prof. RNDr. Jana Jurečková DrSc.
RNDr. Jan Kalina Ph.D.
Ing. Václav Kautský
RNDr. Jana Klicnarová Ph.D.
Mgr. Michal Kupsa Ph.D.
Jarmila Maňhalová
RNDr. Jiří Michálek CSc.
Mgr. Martin Ondreját Ph.D.
RNDr. Jan Seidler CSc.
RNDr. Miroslav Šiman Ph.D.
Mgr. Jakub Slavík Ph.D.
Dr. Jan M. Swart
Ing. Petr Tichavský DSc.
Doc. Petr Volf CSc.
Ing. Karel Vrbenský
Duration: 2006 - 2008
Development of an intelligent information system of maintenance. The system will possess a modular structure containing both basic tools and functions of maintenance management and modules with advanced analytical methods, and including an adaptive knowledge data-basis.
Duration: 2006 - 2010
The project is devoted to basic research in mathematics, probability and statistics, with applications to neurophysiology and economics. A broad new class of random point processes in space and time will be developed and its properties investigated mainly analytically and also by means of simulations. Advanced probabilistic tools will be enhanced to solve complex modeling problems.
Duration: 2005 - 2008
Methods for statistical data analysis and probability modeling play an increasingly important role in learning about natural and social phenomena, especially in connection with the availability and spread of information technology.
Duration: 2004 - 2006
The spacings-based goodnes-of-fit test statistics known from the literature are shown to be asymptotically equivalent to spacings-based Pearson-type statistics. Limit laws will be extended, and the related relative asymptotic efficiencies will be evaluated for new clases of such statistics.