Source: http://staff.utia.cas.cz/vomlel/vomlel.html
Timestamp: 2019-04-25 22:19:16+00:00

Document:
The homepage of the project is here.
 J. Vomlel and M. Studený, Graphical and Algebraic Representatives of Conditional Independence Models. A chapter in Advances in Probabilistic Graphical Models, Series: Studies in Fuzziness and Soft Computing , Vol. 213, Lucas, Peter; Gámez, José A.; Salmerón, Antonio (Eds.), pp. 55-80, Springer, 2007. ISBN: 978-3-540-68994-2. A preliminary version is available here.
on a CP tensor decomposition, International Journal of Approximate Reasoning (2014), Volume 55, Issue 4, pp. 1072-1092, http://dx.doi.org/10.1016/j.ijar.2013.12.002. A preliminary version is available here.
 T. Ottosen and J. Vomlel, All roads lead to Rome—New search methods for the optimal triangulation problem, International Journal of Approximate Reasoning, Vol. 53, Issue 9, 2012, pp. 1350–1366. DOI: 10.1016/j.ijar.2012.06.006 . A preliminary version is available here.
 J. Vomlel, Rank of tensors of l-out-of-k functions: an application in probabilistic inference, Kybernetika, Vol. 47, No. 3, pp. 317-336, 2011. See a version with typos/errors corrected.
 M. Studený and J. Vomlel, On open questions in the geometric approach to structural learning Bayesian nets. International Journal of Approximate Reasoning, Volume 52, Issue 5, July 2011, Pages 627-640.
 M. Studený, J. Vomlel, and R. Hemmecke, A geometric view on learning Bayesian network structures, International Journal of Approximate Reasoning. Vol.51, 5 (2010), pp. 573-586, DOI: 10.1016/j.ijar.2010.01.014 A preliminary version is available here.
International Journal of Approximate Reasoning, Volume 50, Issue 2, February 2009, Pages 385-413. DOI: 10.1016/j.ijar.2008.09.001 . A preliminary version is available here.
 F. Rijmen and J. Vomlel, Assessing the performance of variational methods for mixed logistic regression models, Journal of Statistical Computation and Simulation, Vol. 78, No. 8, August 2008, 765–779. DOI:10.1080/00949650701282507 . A preliminary version is available here.
 P. Savický and J. Vomlel, Exploiting tensor rank-one decomposition in probabilistic inference, Kybernetika, Vol. 43, Number 5 (Special Issue dedicated to the memory of Albert Perez), pp. 747-764, 2007. An almost final version is available here.
 J. Vomlel, Noisy-or classifier. International Journal of Intelligent Systems, Volume 21, Issue 3 (March 2006), pp. 381-398. A preliminary version (but with several typos corrected) is available here. The Reuters dataset (preprocessed by G. Karciauskas) used for experiments is available here. The C++ code that implements the learning and testing of the noisy-or classifier is available on request.
 J. Vomlel, Probabilistic reasoning with uncertain evidence, Neural Network World, International Journal on Neural and Mass-Parallel Computing and Information Systems, Vol. 14, No. 5/2004, pp. 453-465.
 J. Vomlel, Integrating inconsistent data in a probabilistic model, Journal of Applied Non-Classical Logics, Vol. 14, No. 3/2004, pp. 365-386. A preliminary version is available here.
 Y.-G. Kim, M. Valtorta, J. Vomlel, A Prototypical System for Soft Evidential Update, Applied Intelligence, Vol. 21, Issue 1, July - August 2004, pp. 81 - 97.
 J. Vomlel: Bayesian networks in educational testing, International Journal of Uncertainty, Fuzziness and Knowledge Based Systems, Vol. 12, Supplementary Issue 1, 2004, pp. 83-100. A draft version.
 M. Vomlelová and J. Vomlel: Troubleshooting: NP-hardness and solution methods, Soft Computing Journal, Volume 7, Number 5, April 2003, pp. 357-368. Online version available from SpringerLink and a draft version (with improved AO* algorithm).
 P. Tichavský, J. Vomlel. Representations of Bayesian networks by low-rank models, International Conference on Probabilistic Graphical Models, 11-14 September 2018, Prague. Proceedings of Machine Learning Research, Volume 72, pp. 463-474.
 M. Plajner and J. Vomlel. Monotonicity in Bayesian Networks for Computerized Adaptive Testing. In A. Antonucci et al. (Eds.): ECSQARU 2017, Springer LNAI 10369, pp. 125–134, 2017.
 J. Vomlel and V. Kratochvíl. Solving Trajectory Optimization Problems by Influence Diagrams. In A. Antonucci et al. (Eds.): ECSQARU 2017, Springer LNAI 10369, pp. 125–134, 2017.
 M. Plajner and J. Vomlel. Student Skill Models in Adaptive Testing. Proceedings of the Eighth International Conference on Probabilistic Graphical Models, pp. 403–414, 2016. JMLR Workshop and Conference Proceedings, Volume 52.
 M. Plajner and J. Vomlel. Bayesian network models for adaptive testing. In the Proceedings of the Twelfth Annual Bayesian Modeling Applications Workshop, Amsterdam, Netherlands, 2015.
 V. Kratochvíl and J. Vomlel. Influence diagrams for the optimization of a vehicle speed profile. In the Proceedings of the Twelfth Annual Bayesian Modeling Applications Workshop, Amsterdam, Netherlands, 2015.
 J. Vomlel and P. Tichavský. An Approximate Tensor-Based Inference Method Applied to the Game of Minesweeper. In the Proceedings of the Seventh European Workshop on Probabilistic Graphical Models (PGM 2014), Utrecht, The Netherlands, September 17-19, 2014, Springer LNAI 8745, pp. 535-550. A preliminary version is available here.
 J. Vomlel and P. Tichavský. Probabilistic Inference in BN2T Models by Weighted Model Counting.
threshold models based on a CP tensor decomposition. In the Proceedings of the Sixth European Workshop on Probabilistic Graphical Models (PGM 2012), Granada, Spain, September 19-21, 2012, pp. 355-362.
 T. Ottosen and J. Vomlel, All roads lead to Rome - New search methods for optimal triangulations. In the Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM 2010), Helsinki, Finland, September 13-15, pp. 201-208, 2010.
 T. Ottosen and J. Vomlel, Honour thy neighbour - Clique maintenance in dynamic graphs. In the Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM 2010), Helsinki, Finland, September 13-15, pp. 209-216, 2010.
 P. Savický and J. Vomlel, Triangulation heuristics for BN2O networks. In C. Sossai and G. Chemello (Eds.): ECSQARU 2009, Springer LNAI 5590, pp. 566–577, 2009. ISBN: 978-3-642-02905-9. Online version available from Springer.
 J. Vomlel and P. Savický, Arithmetic circuits of the noisy-or models. In the Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM'08), Hirtshals, Denmark, September 17-19, 2008, pp. 297-304. Detailed results and tested models are available here.
 M. Studený and J. Vomlel, A Geometric Approach to Learning BN Structures. In the Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM'08), Hirtshals, Denmark, September 17-19, 2008, pp. 281-288. An extended version of this paper and a web page related to this paper.
 P. Savický and J. Vomlel, Tensor rank-one decomposition of probability tables, In the Proceedings of the 11th IPMU conference, Paris, France, July 2-7, 2006, pp. 2292-2299. See the extended version published in Kybernetika.
 M. Studený and J. Vomlel, Transition between graphical and algebraic representatives of Bayesian network models (an extended version), In Proceeding of the 2nd European Workshop on Probabilistic Graphical Models (PGM'04), Leiden, the Netherlands. See the extended version published in International Journal of Approximate Reasoning.
Neural Network World, International Journal on Neural and Mass-Parallel Computing and Information Systems.
 J. Vomlel: Noisy-or classifier, Proceedings of the 6th Workshop on Uncertainty Processing (WUPES 2003), Hejnice, September 2003, pp. 291-302. See the extended version published in International Journal of Intelligent Systems.
 J. Vomlel: Integrating inconsistent data in a probabilistic model, Proceedings of the Uncertainty, Incompleteness, Imprecision and Conflict in Multiple Data Sources, an affiliate workshop to ECSQARU'03, Aalborg, 2003. See the extended version published in Journal of Applied Non-Classical Logics.
 J. Vomlel: Two applications of Bayesian networks, In Proceedings of conference Znalosti 2003 February 2003, Ostrava, Czech Republic, pp. 73-82. K dispozici je i ceska verze. See the extended version published in Kybernetika.
 J. Vomlel: Bayesian Networks in Educational Testing, In Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM'02), November 6-8, 2002, Cuenca, Spain, pp. 176-185. See the extended version published in International Journal of Uncertainty, Fuzziness and Knowledge Based Systems.
 J. Vomlel: Exploiting Functional Dependence in Bayesian Network Inference, In Proceedings of The 18th Conference on Uncertainty in Artificial Intelligence (UAI 2002), August 1-4, 2002, University of Alberta, Edmonton, Canada, pp. 528-535.
 J. Vomlel and C. Skaanning: Troubleshooting with Simultaneous Models. In: S. Benferhat, P. Besnard (Eds.): Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 6th European Conference, ECSQARU 2001, Toulouse, France, September 19-21, 2001, Proceedings. On line version available from Springer, and an almost final draft.
 J. Vomlel and V. Kratochvíl. Dynamic Bayesian Networks for the Classification of Sleep Stages. In Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18), Třeboň, Czech Republic, 2018, pp. 205-215.
 J. Švorc and J. Vomlel. Employing Bayesian Networks for Subjective Well-being Prediction. In Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18), Třeboň, Czech Republic, 2018, pp. 189-204.
 M. Plajner and J. Vomlel. Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions. In Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18), Třeboň, Czech Republic, 2018, pp. 153-164.
 V. Djordjilović, M. Chiogna, J. Vomlel. An empirical comparison of popular algorithms for learning gene networks. In Proceedings of the 10th Workshop on Uncertainty Processing (WUPES’15), Monínec, Czech Republic, 2015, pp. 61-72.
 J. Vomlel and V. Kratochvíl. Influence diagrams for speed profile optimization: computational issues. In Proceedings of the 10th Workshop on Uncertainty Processing (WUPES’15), Monínec, Czech Republic, 2015, pp. 203-216.
 J. Vomlel and P. Tichavsky. On tensor rank of conditional probability tables in Bayesian networks. A preprint arXiv:1409.6287, available from arXiv.org. My poster from Prague Stochastics 2014 conference.
 J. Vomlel. A Generalization of the Noisy-Or Model. A preprint submitted to Kybernetika Journal.
 J. Vomlel. A generalization of the noisy-or model to multivalued parent variables. In The Proceedings of the 16th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty (CJS-2013), Mariánské Lázně, Czech Republic, September 19-22, 2013, pp. 19-27.
Mortality Prediction in Patients with ST Elevation Myocardial Infarction. In the Proceedings of The Nineth Workshop on Uncertainty Processing WUPES'12, Mariánské Lázně, Czech Republic, September 12-15th, 2012, pp. 204-213.
 V. Kratochvíl, H. Kružík, P. Tůma, J. Vomlel a P. Somol. Predikce hospitalizační mortality u akutního infarktu myokardu. (In Czech). Sborník příspěvků konference MEDSOFT 2011, str. 128-138.
 M. Studený and J. Vomlel. On open questions in the geometric approach to learning BN structures. In the proceedings of The Eighth Workshop on Uncertainty Processing WUPES'09, Liblice, Czech Republic, September 19-23th, 2009, pp. 226-236.
 J. Vomlel and P. Savický. An experimental comparison of triangulation heuristics on transformed BN2O networks. In the proceedings of The Eighth Workshop on Uncertainty Processing WUPES'09, Liblice, Czech Republic, September 19-23th, 2009, pp. 251-260.
 M. Vomlelová and J. Vomlel. Applying Bayesian networks in the game of Minesweeper. In the Proceedings of the Twelfth Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, Litomyšl, Czech Republic, September, 2009, pp. 153-162.
 J. Vomlel and M. Studený. Using imsets for learning Bayesian networks. In the Proceedings of the Tenth Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, Liblice, Czech Republic, September, 2007, pp. 178-189.
 R. Jiroušek, V. Kratochvíl, T. Kroupa, R. Lněnička, M. Studený, J. Vomlel, P. Hampl, and H. Hamplová, An evaluation of string similarity measures on pricelists of computer components. In the Proceedings of the Tenth Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, Liblice, Czech Republic, September, 2007, pp. 69-74.
M. Studený and J. Vomlel (Editors). Proceedings of the third European Workshop on Probabilistic Graphical Models (PGM'06). Prague, September 12-15, 2006.
 J. Vomlel, Decomposition of Probability Tables Representing Boolean Functions. In the Proceedings of the Eighth Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, Trest, Czech Republic, September, 18 - 21, 2005.
 P. Savický, J. Vomlel, Tensor rank-one decomposition of probability tables. Research report, DAR-UTIA 2005/26, Praha (an extended version of the IPMU 2006 paper containing a brief description of a numerical algorithm for finding tensor-rank one decompositions).
 J. Vomlel, Bayesian networks in Mastermind, Proceeding of the 7th Czech-Japan Seminar, Awaji Island, Japan.
 M. Sochorová and J. Vomlel: Troubleshooting: NP-hardness and solution methods, The Fifth Workshop on Uncertainty Processing WUPES 2000, Jindrichuv Hradec, Czech Republic, 20-24th June 2000. See the extended version published in Soft Computing Journal.
 J. Vomlel: Methods of Probabilistic Knowledge Integration (PhD Thesis) and the abstract.
 J. Vomlel: Statistical Methods for Probabilistic Model Parameter Estimation from Incomplete Data and their Application to the Marginal Problem, In: Proc. of WUPES'97, pp. 184-193, January 1997, Prague.
 J. Vomlel: Dependency Models, Draft Paper, Institute of Information Theory and Automation, 1996, Prague.
 J. Vomlel: Probabilistic models in Artificial Intelligence, Research Report, Czech Technical University, 1995, Prague.
 R. Jiroušek and J. Vomlel: Inconsistent knowledge integration in a probabilistic model, In: Proc. of Workshop Mathematical Models for handling partial knowledge in A.I., pp. 263-270, Plenum Publ. Corp., 1994, Erice, Sicily.
 A set consisting of 9 Bayesian networks of the bn2o type used in the probabilistic reasoning evaluation at UAI'08.
 Umělá inteligence pro optimalizaci pohybu rakety. Prezentace na Týdnu vědy a techniky AV ČR, listopad 2017.
 Řízení rychlosti vozu Formule 1 pomocí rozhodovacího diagramu. Prezentace na MFF UK v rámci předmětu Matematické problémy nematematiků. Video of Sebastian Vettel at GP in Silverstone.
 Jirka Vomlel and Petr Tichavsky. Probabilistic Inference in BN2T Models by Weighted Model Counting. Presentation at the 12th Scandinavian AI conference held at the Aalborg University, Aalborg, Denmark.
 Probabilistic graphical models: current research activities. Presentation at PhD symposium at SCAI, Aalborg, Denmark, November 2013.
 Kdo má spolehlivý recept na správné rozhodování? Prezentace na Týdnu vědy a techniky AV ČR, listopad 2013. Video přenos z prezentace je dostupný on line.
 Computationally efficient probabilistic inference with noisy threshold models based on a CP tensor decomposition. Presentation at the Sixth European Workshop on Probabilistic Graphical Models (PGM 2012), Granada, Spain.
 Machine Learning Methods for Mortality Prediction in Patients with ST Elevation Myocardial Infarction. Presentation at the Nineth Workshop on Uncertainty Processing WUPES'12, Mariánské Lázně, Czech Republic.
 Rank of tensors of l-out-of-k functions: an application in probabilistic inference. Presentation at the DAR conference at Marianska, Jachymov, Czech Republic.
 Predikce hospitalizační mortality u akutního infarktu myokardu. Prezentace na konferenci MEDSOFT 2011, Roztoky u Prahy.
 Noisy logical connectives in Bayesian networks. Presentation at the Dipleap workshop in Vienna organized within the framework of the ESF/Eurocores program LogICCC.
 Causal Semantics of Bayesian Networks. Presentation at Probnet 2010 workshop in Salzburg.
 Jádro koaliční hry - algoritmy. Prezentace na semináři skupiny IS ČSKI a projektu DAR v ÚTIA.
 Arithmetic Circuits of the Noisy-Or Models. Presentation at the Fourth European Workshop on Probabilistic Graphical Models (PGM'08), Hirtshals, Denmark, September 17-19, 2008.
 Tensor rank-one decomposition of noisy-or models. Presentation at Alsovice seminar, held in Rakvice, Czech Republic, June, 2008.
 Using imsets for learning Bayesian networks. Presentation at the Tenth Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, Liblice, Czech Republic, September, 2007.
 An evaluation of string similarity measures on pricelists of computer components. Presentation at the Tenth Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, Liblice, Czech Republic, September, 2007.
Rank-one decomposistion of probability tables. Presentation at WUPES'06 - the 7th Workshop on Uncertainty Processing held in Mikulov, Czech Republic.
 Tensor rank-one decomposition of probability tables. Presentation at the 11th IPMU conference in Paris.
 A variational method for the Rasch model. Presentation at the Czech-Austrian workshop PROBNET 2005.
 What are imsets and what they are good for. Presentation at VRSR 2005 seminar in Ricky v Orlickych horach.
 Metody pro zpracovani strukturovaneho textu: porovnavani ceniku. Prednaska na 2. konferenci vyzkumneho centra DAR.
 Bayesian networks in educational testing. My presentation at Nijmeegs instituut voor informatica en informatiekunde colloquium, Radboud University Nijmegen, Netherlands.
 Bayesian networks in educational testing. My presentation at the research seminar Quantitative Methods at the Department of Psychology, Catholic University Leuven.
 Klasifikace metodou logisticke regrese. Prednaska v ramci predmetu Strojove uceni na MFF UK.
 Applikace bayesovskych siti. Prednaska na seminari kvantitativni metodologie na FTVS UK.
 Some applications of Bayesian networks. My presentation at a seminar of the Czech Society for Cybernetics and Informatics (CSKI). A paper-saving PostScript version to print.
 Integrating inconsistent data in a probabilistic model. My presentation at Salzburg 2004 workshop and a paper-saving PostScript version to print.
 Probabilistic reasoning with uncertain evidence, My presentation for Working Group on Theoretical Robotics of the Czech Society for Cybernetics and Informatics (CSKI) A paper-saving PostScript version to print.
 Implementation of Imsets in the R language (a short presentation after Milan Studeny's talk at PGM'04).
 Uvod do Bayesovskych siti, prednaska v ramci predmetu Medicinska informatika. K dispozici je i PostScriptova verze pro tisk.
 Bayesian networks in Mastermind, my presentation at the 7th Czech-Japan Seminar, Awaji Island, Japan. A paper-saving PostScript version to print.
 Thoughts on belief and model revision with uncertain evidence, my presentation at conference Znalosti 2004. A paper-saving PostScript version to print.
 Noisy-or classifier, my presentation at WUPES 2003. A paper-saving PostScript version to print.
 Integrating inconsistent data in a probabilistic model, my presentation at an affiliate workshop to ECSQARU 2003 "Uncertainty, Incompleteness, Imprecision and Conflict in Multiple Data Sources". A paper-saving PostScript version to print.
My presentation for Working Group on Theoretical Robotics of the Czech Society for Cybernetics and Informatics (CSKI). A paper-saving PostScript version to print.
My presentation for students from Nederlands in UTIA in April 2003. A paper-saving PostScript version to print.
My presentation at conference Znalosti 2003 held in February 2003 in Ostrava, Czech Republic. A paper-saving PostScript version to print.
My presentation at the PGM'02 workshop and a paper-saving PostScript version to print.
My presentation within the LISP seminar series and a paper-saving PostScript version to print.
 Visualizing and Exploring Data (my presentation within Aalborg BSS data mining tutorial series).
Efficient Propagation for Computerized Adaptive Testing (my presentation within the Aalborg BSS seminar series).
 Troubleshooting with Simultaneous Models, Symbolic and Quantitative Approaches to Reasoning with Uncertainty 6th European Conference, ECSQARU 2001, Toulouse, France, September 19-21.
 Game Networks (my presentation within Aalborg BSS seminar series) based mainly on the UAI'2000 paper "Game Networks by Piero La Mura".
 Methods of Probabilistic Knowledge Integration (overheads from the defence of my PhD Thesis).
 Troubleshooting: NP-hardness and solution methods, The Fifth Workshop on Uncertainty Processing WUPES'2000, Jindrichuv Hradec, Czech Republic, 20-24th June 2000.
 Přednáška Příklady aplikací bayesovských sítí, v rámci předmětu PRAVDĚPODOBNOSTNÍ MODELY UMĚLÉ INTELIGENCE, Fakulta jaderná a fyzikálně inženýrská, České vysoké učení technické v Praze.
 Přednáška Příklady aplikací bayesovských sítí, v rámci předmětu Teorie informace a inference Vysoká škola ekonomická v Praze. Bonus: Výpočty pomocí metody založené ma stromech spojení.
 Přednáška Bayesovské sítě: pravděpodobnostní inference a aplikace, v rámci předmětu PRAVDĚPODOBNOSTNÍ MODELY UMĚLÉ INTELIGENCE, Fakulta jaderná a fyzikálně inženýrská, České vysoké učení technické v Praze.
 Přednáška Příklady aplikací bayesovských sítí v rámci předmětu 4IZ410 - Teorie informace a inference na VŠE Praha.
 Přednáška Inference v bayesovských sítích a Aplikace bayesovských sítí, v rámci předmětu PRAVDĚPODOBNOSTNÍ MODELY UMĚLÉ INTELIGENCE, Fakulta jaderná a fyzikálně inženýrská, České vysoké učení technické v Praze.
2009] Přednáška "Úvod do bayesovských sítí" v rámci kurzu "Počítačová podpora diagnostiky a terapie".
Soubory wet_grass.net, two_coins_1.net, two_coins_2.net, asia.net a monty_hall.net pro spuštění v programu Hugin Lite.
 Přednáška "Úvod do bayesovských sítí" v rámci kurzu "Počítačová podpora diagnostiky a terapie".
Based on the manuscript Metody reprezentace a zpracovani znalosti v umele inteligenci by Radim Jirousek.
Geneticke algoritmy, usporna verze pro tisk a ukazkovy program prevzaty z www.codeproject.com .
Linearni rozhodovaci funkce. Kapitola 3 knihy Metody reprezentace a zpracovani znalosti v umele inteligenci.
Chyba rozhodovani, usporna verze pro tisk. Ukazkovy system pro vizulizaci dat (Ggobi) je k dispozici na www.ggobi.org .
Uvod do bayesovskych siti , usporna verze pro tisk a ukazka rozhodovaciho diagramu ve formatu Hugin NET.
Pouziti bayesovskych siti pro testovani znalosti a usporna verze pro tisk.
 Managing uncertainty in Artficial Intelligence (Zpracovani nejistoty v umele inteligenci, IZI462), University of Economics, Prague. Advertising poster.
Approximate propagation in Bayesian networks, my lecture within DAT5 course at Aalborg University.
Learning parameters of Bayesian Networks, my lecture within DAT5 course at Aalborg University.

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