4 citations · 15 across the 7 of their papers we have counts for
6 papers · 1 filter
Solving the Goddard problem by an influence diagram
Jiří Vomlel, Václav Kratochvíl
Influence diagrams are a decision-theoretic extension of probabilistic graphical models. In this paper we show how they can be used to solve the Goddard problem. We present results…
Probabilistic Models for Computerized Adaptive Testing: Experiments
Martin Plajner, Jiří Vomlel
This paper follows previous research we have already performed in the area of Bayesian networks models for CAT. We present models using Item Response Theory (IRT - standard CAT met…
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…
On tensor rank of conditional probability tables in Bayesian networks
Jiří Vomlel, Petr Tichavský
A difficult task in modeling with Bayesian networks is the elicitation of numerical parameters of Bayesian networks. A large number of parameters is needed to specify a conditional…
Exploiting Functional Dependence in Bayesian Network Inference
Jirka Vomlel
We propose an efficient method for Bayesian network inference in models with functional dependence. We generalize the multiplicative factorization method originally designed by Tak…