# Publications

## Book Chapters

[2007] 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.

## Journal Publications

[2021] F. Kratochvíl, D. Moeljadi, B. Delpada, V. Kratochvíl, and J. Vomlel. Aspectual pairing and aspectual classes in Abui. STUF - Language Typology and Universals, vol. 74, no. 3-4, 2021, pp. 621-657. https://doi.org/10.1515/stuf-2021-1046

[2020] M. Plajner and J. Vomlel, Learning bipartite Bayesian networks under monotonicity restrictions, International Journal of General Systems, Volume 49, Issue 1, 2020, pp. 88-111. https://doi.org/10.1080/03081079.2019.1692004

[2018] J. Jurčák, R. Rezaei, N. Bello González, R. Schlichenmaier and J. Vomlel, The magnetic nature of umbra–penumbra boundary in sunspots. Astronomy & Astrophysics, Volume 611 (March 2018), L4, Pages 1-13. https://doi.org/10.1051/0004-6361/201732528

[2017] V. Kratochvíl and J. Vomlel, Influence diagrams for speed profile optimization. International Journal of Approximate Reasoning.Volume 88, September 2017, Pages 567-586. https://doi.org/10.1016/j.ijar.2016.11.018

[2017] V. Djordjilović, M. Chiogna, J. Vomlel, An empirical comparison of popular structure learning algorithms with a view to gene network inference. International Journal of Approximate Reasoning. Volume 88, September 2017, Pages 602-613. https://dx.doi.org/10.1016/j.ijar.2016.12.012

[2015] J. Vomlel, Generalizations of the noisy-or model. Kybernetika., Volume 51, Issue 3 (A special issue dedicated to the memory of Ivan Kramosil), 2015, pp. 508-524. http://dx.doi.org/10.14736/kyb-2015-3-0508

[2014] J. Vomlel and P. Tichavský, Probabilistic inference with noisy-threshold models based 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.

[2012] 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.

[2011] 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.

[2011] 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.

[2010] 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.

[2008] M. Studený and J. Vomlel, A reconstruction algorithm for the essential graph, 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.

[2008] 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.

[2007] 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.

[2006] J. Vomlel, Noisy-or classifier. International Journal of Intelligent Systems, Volume 21, Issue 3 (March 2006), pp. 381-398. A preliminary version (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.

[2004] 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.

[2004] 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.

[2004] J. Vomlel, Building Adaptive Tests using Bayesian networks, Kybernetika, Volume 40, Number 3, 2004, pp. 333 - 348.

[2004] 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.

[2004] 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.

[2003] 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).

[2002] M. Valtorta, Y.-G. Kim, and J. Vomlel: Soft Evidential Update for Multiagent Systems, International Journal of Approximate Reasoning, Volume 29, Issue 1, January 2002, pp. 71-106. (an almost final draft)

[2001] F. V. Jensen, U. Kjaerulff, B. Kristiansen, H. Langseth, C. Skaanning, J. Vomlel & M. Vomlelová: The SACSO methodology for troubleshooting complex systems. Special Issue on AI in Equipment Service, Artificial Intelligence for Engineering Design, Analysis and Manufacturing (AIEDAM), Vol. 15, pp. 321-333, 2001. (an almost final draft)

## Publications in Peer Reviewed Conference Proceedings

[2022] I. Pérez and J. Vomlel. On the rank of 2×2×2 probability tables. In Proceedings of The 11th International Conference on Probabilistic Graphical Models (PGM 2022),

Proceedings of Machine Learning Research (PMLR), Vol. 186, pages 361-372.

[2022] F. Kratochvíl, V. Kratochvíl, and J. Vomlel. Learning Noisy-Or Networks with an Application in Linguistics. In Proceedings of The 11th International Conference on Probabilistic Graphical Models (PGM 2022), Proceedings of Machine Learning Research (PMLR) Vol. 186, pages 277-288.

[2021] Plajner M., Vomlel J. Bayesian Networks for the Test Score Prediction: A Case Study on a Math Graduation Exam. In: Vejnarová J., Wilson N. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2021. Lecture Notes in Computer Science, vol 12897. Springer, Cham. https://doi.org/10.1007/978-3-030-86772-0_19. See the extended version with the corrected error in Proposition 1: O(nz) should be O((nz)^2).

[2018] 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.

[2017] 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. https://dx.doi.org/10.1007/978-3-319-61581-3

[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. https://dx.doi.org/10.1007/978-3-319-61581-3

[2016] 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.

[2015] 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.

[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.

[2014] 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.

[2013] J. Vomlel and P. Tichavský. Probabilistic Inference in BN2T Models by Weighted Model Counting. In the Proceedings of the Twelfth Scandinavian Conference on Artificial Intelligence, M. Jaeger et al. (Eds.), IOS Press, pp. 275-284, 2013. doi:10.3233/978-1-61499-330-8-275

[2012] J. Vomlel and P. Tichavský, Computationally efficient probabilistic inference with noisy 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.

[2010] 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.

[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.

[2009] 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.

[2008] 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.

[2008] 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.

[2006] 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.

[2004] 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.

[2004] J. Vomlel: Thoughts on belief and model revision with uncertain evidence, Proceedings of the conference Znalosti 2004, Brno, February 2004, pp. 126-137. See the extended version published in Neural Network World, International Journal on Neural and Mass-Parallel Computing and Information Systems.

[2003] 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.

[2003] 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.

[2003] 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.

[2002] 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.

[2002] 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.

[2001] 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.

## Other Publications

[2022] F. Kratochvíl, V. Kratochvíl, G. Saad, J. Vomlel. Modeling the spread of loanwords in South-East Asia using sailing navigation software and Bayesian networks. Proceedings of the 12th Workshop on Uncertainty Processing, p. 135-146 , Eds: Studený Milan, Ay Nihat, Coletti Giulianella, Kleiter Gernot D., Shenoy Prakash P., WUPES 2022: 12th Workshop on Uncertainty Processing, (Kutná Hora, CZ, 20220601)

[2020] S. Carpitella, J. Izquierdo, M. Plajner, and J. Vomlel. Integrating the human factor in FMECA-based risk evaluation through Bayesian networks. In Conference Proceedings of Mathematical Modelling for Engineering & Human Behaviour 2020, Valencia, Spain.

[2019] J. Švorc and J. Vomlel. Bayesian Networks for the Analysis of Subjective Well-Being. In Proceedings of the 22nd Czech-Japan Seminar on Data Analysis and Decision Making (CJS’19), Nový Světlov, Czech Republic, Editors: M. Inuiguchi, R. Jiroušek, V. Kratochvíl, September 25 - 28, 2019, pp. 175-188.

[2018] 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.

[2018] 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.

[2018] 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.

[2017] M. Plajner, A. Magauina, and J. Vomlel. Question Selection Methods for Adaptive Testing with Bayesian Networks. Proceedings of the 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, CZECH-JAPAN SEMINAR 2017, Editors: Vilém Novák, Masahiro Inuiguchi, Martin Štěpnička, Pardubice, Czech Republic, September 17–20, 2017, pp. 164-175. http://irafm.osu.cz/cjs2017/materials/cjs2017proceedings.pdf

[2017] I. Salman and J. Vomlel. A machine learning method for incomplete and imbalanced medical data. In Proceedings of the 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, CZECH-JAPAN SEMINAR 2017, Editors: Vilém Novák, Masahiro Inuiguchi, Martin Štěpnička, Pardubice, Czech Republic, September 17–20, 2017, pp. 188-195. http://irafm.osu.cz/cjs2017/materials/cjs2017proceedings.pdf

[2015] 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.

[2015] 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.

[2014] 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.

[2014] J. Vomlel. A Generalization of the Noisy-Or Model. A preprint submitted to Kybernetika Journal.

[2013] 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.

[2012] J. Vomlel, H. Kružík, P. Tůma, J. Přeček, and M. Hutyra. Machine Learning Methods for 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.

[2011] 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.

[2009] 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.

[2009] 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.

[2009] 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.

[2007] 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.

[2007] 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.

[2006] M. Studený and J. Vomlel (Editors). Proceedings of the third European Workshop on Probabilistic Graphical Models (PGM'06). Prague, September 12-15, 2006.

[2005] 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, Třešť, Czech Republic, September, 18 - 21, 2005.

[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).

[2004] J. Vomlel, Bayesian networks in Mastermind, Proceeding of the 7th Czech-Japan Seminar, Awaji Island, Japan.

[2000] 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.

[1999] J. Vomlel: Methods of Probabilistic Knowledge Integration (PhD Thesis) and the abstract.

[1997] 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.

[1996] J. Vomlel: Dependency Models, Draft Paper, Institute of Information Theory and Automation, 1996, Prague.

[1995] J. Vomlel: Probabilistic models in Artificial Intelligence, Research Report, Czech Technical University, 1995, Prague.

[1994] 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.

## Software and datasets

[2008] A set consisting of 9 Bayesian networks of the bn2o type used in the probabilistic reasoning evaluation at UAI'08.

[2007] imset.R - a suite of functions for R (implementing also a learning algorithm for Bayesian networks)