Jirí Vomlel

发表

Solving the Goddard problem by an influence diagram pdf

Jirí Vomlel, Václav Kratochvíl, 2017, ArXiv.

Generalizations of the noisy-or model

Jirí Vomlel, Jirí Vomlel, Jiří Vomlel, 2015, Kybernetika.

Monotonicity in Bayesian Networks for Computerized Adaptive Testing

Jirí Vomlel, Martin Plajner, J. Vomlel, 2017, ECSQARU.

Prediction Model of Inpatient Mortality for Patients with Myocardial Infarction

Jirí Vomlel, Václav Kratochvíl, Petr Somol, 2012, HEALTHINF.

Influence diagrams for speed profile optimization

Jirí Vomlel, Václav Kratochvíl, V. Kratochvíl, 2017, Int. J. Approx. Reason..

Bayesian Networks In Educational Testing

Jirí Vomlel, Jirka Vomlel, 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

Building adaptive tests using Bayesian networks

Jirí Vomlel, J. Vomlel, Jirka Vomlel, 2004, Kybernetika.

Solving Trajectory Optimization Problems by Influence Diagrams

Jirí Vomlel, Václav Kratochvíl, V. Kratochvíl, 2017, ECSQARU.

Solving the Brachistochrone Problem by an Influence Diagram pdf

Jirí Vomlel, 2017, ArXiv.

Exploiting tensor rank-one decomposition in probabilistic inference

Jirí Vomlel, Petr Savický, P. Savický, 2007, Kybernetika.

All roads lead to Rome - New search methods for the optimal triangulation problem

Jirí Vomlel, Thorsten J. Ottosen, Jirí Vomlel, 2012, Int. J. Approx. Reason..

Soft evidential update for probabilistic multiagent systems

Jirí Vomlel, Young-Gyun Kim, Marco Valtorta, 2002, Int. J. Approx. Reason..

Editorial Note

Jirí Vomlel, Milan Studený, Milan Studený, 2008, Int. J. Approx. Reason..

Probabilistic Inference in BN2T Models by Weighted Model Counting

Jirí Vomlel, Petr Tichavský, P. Tichavský, 2013, SCAI.

The SACSO methodology for troubleshooting complex systems

Jirí Vomlel, Claus Skaanning, Helge Langseth, 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

Influence Diagrams for the Optimization of a Vehicle Speed Profile pdf

Jirí Vomlel, Václav Kratochvíl, V. Kratochvíl, 2015, BMA@UAI.

Troubleshooting: NP-hardness and solution methods

Jirí Vomlel, Marta Vomlelová, 2003, Soft Comput..

Triangulation Heuristics for BN2O Networks

Jirí Vomlel, Petr Savický, P. Savický, 2009, ECSQARU.

A Prototypical System for Soft Evidential Update

Jirí Vomlel, Young-Gyun Kim, Marco Valtorta, 2004, Applied Intelligence.

An empirical comparison of popular structure learning algorithms with a view to gene network inference

Monica Chiogna, Jirí Vomlel, Vera Djordjilovic, 2017, Int. J. Approx. Reason..

Exploiting Functional Dependence in Bayesian Network Inference

Jirí Vomlel, Jirka Vomlel, 2002, UAI.

Bayesian Network Models for Adaptive Testing pdf

Jirí Vomlel, Martin Plajner, M. Plajner, 2015, BMA@UAI.

Probabilistic inference with noisy-threshold models based on a CP tensor decomposition

Jirí Vomlel, Petr Tichavský, P. Tichavský, 2014, Int. J. Approx. Reason..

Rank of tensors of l-out-of-k functions: an application in probabilistic inference

Jirí Vomlel, 2011, Kybernetika.

An Approximate Tensor-Based Inference Method Applied to the Game of Minesweeper

Jirí Vomlel, Petr Tichavský, 2014, Probabilistic Graphical Models.

Integrating inconsistent data in a probabilistic model

Jirí Vomlel, Jirí Vomlel, 2004, J. Appl. Non Class. Logics.

Troubleshooting with Simultaneous Models

Jirí Vomlel, Claus Skaanning, 2001, ECSQARU.

On tensor rank of conditional probability tables in Bayesian networks pdf

Jirí Vomlel, Petr Tichavský, 2014, ArXiv.

Probabilistic Models for Computerized Adaptive Testing: Experiments pdf

Jirí Vomlel, Martin Plajner, J. Vomlel, 2016, ArXiv.

Representations of Bayesian networks by low-rank models

Jirí Vomlel, Petr Tichavský, 2018, PGM.

A reconstruction algorithm for the essential graph

Jirí Vomlel, Milan Studený, M. Studený, 2009, Int. J. Approx. Reason..

On open questions in the geometric approach to structural learning Bayesian nets

Jirí Vomlel, Milan Studený, M. Studený, 2011, Int. J. Approx. Reason..

Student Skill Models in Adaptive Testing

Jirí Vomlel, Martin Plajner, J. Vomlel, 2016, Probabilistic Graphical Models.

A geometric view on learning Bayesian network structures

Jirí Vomlel, Milan Studený, Raymond Hemmecke, 2010, Int. J. Approx. Reason..

Noisy‐or classifier

Jirí Vomlel, Jirí Vomlel, 2006, Int. J. Intell. Syst..

Bayesian Networks for the Test Score Prediction: A Case Study on a Math Graduation Exam

Jirí Vomlel, Martin Plajner, J. Vomlel, 2021, ECSQARU.