999 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Czech Academy of SciencesCZ67 papers
- Charles UniversityCZ39 papers
- Czech Technical University in PragueCZ33 papers
- Johannes Kepler University of LinzAT7 papers
- University of South Bohemia in České BudějoviceCZ6 papers
- University of WarwickGB6 papers
- V. N. Karazin Kharkiv National UniversityUA6 papers
- Federation UniversityAU5 papers
- Prague University of Economics and BusinessCZ5 papers
- Technical University of LiberecCZ5 papers
- Czech Academy of Sciences, Institute of ThermomechanicsCZ4 papers
- Friedrich-Alexander-Universität Erlangen-NürnbergDE4 papers
9 papers · 1 filter
Influence diagrams for the optimization of a vehicle speed profile
Václav Kratochvíl, Jiří Vomlel
Influence diagrams are decision theoretic extensions of Bayesian networks. They are applied to diverse decision problems. In this paper we apply influence diagrams to the optimizat…
Bayesian Network Models for Adaptive Testing
Martin Plajner, Jiří Vomlel
Computerized adaptive testing (CAT) is an interesting and promising approach to testing human abilities. In our research we use Bayesian networks to create a model of tested humans…
Decision-Theoretic Troubleshooting: Hardness of Approximation
Václav Lín
Decision-theoretic troubleshooting is one of the areas to which Bayesian networks can be applied. Given a probabilistic model of a malfunctioning man-made device, the task is to co…
On Separation Criterion and Recovery Algorithm for Chain Graphs
Milan Studeny
Chain graphs give a natural unifying point of view on Markov and Bayesian networks and enlarge the potential of graphical models for description of conditional independence structu…
Composition of Probability Measures on Finite Spaces
Radim Jirousek
Decomposable models and Bayesian networks can be defined as sequences of oligo-dimensional probability measures connected with operators of composition. The preliminary results sug…
Bayesian Networks from the Point of View of Chain Graphs
Milan Studeny
AThe paper gives a few arguments in favour of the use of chain graphs for description of probabilistic conditional independence structures. Every Bayesian network model can be equi…
Conditional Independence and Markov Properties in Possibility Theory
Jirina Vejnarova
Conditional independence and Markov properties are powerful tools allowing expression of multidimensional probability distributions by means of low-dimensional ones. As multidimens…
Marginalization in Composed Probabilistic Models
Radim Jirousek
Composition of low-dimensional distributions, whose foundations were laid in the papaer published in the Proceeding of UAI'97 (Jirousek 1997), appeared to be an alternative apparat…
On characterizing Inclusion of Bayesian Networks
Tomas Kocka, Remco R. Bouckaert, Milan Studeny
Every directed acyclic graph (DAG) over a finite non-empty set of variables (= nodes) N induces an independence model over N, which is a list of conditional independence statements…