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Publications - Milan Studený


Books and chapters (7)
1. * Milan Studený: Conditional Independence and Basic Markov Properties. Handbook of Graphical Models, 3-38. CRC Press, Boca Raton 2018.   Download
2. * D. Haws, J. Cussens, Milan Studený: Polyhedral approaches to learning Bayesian networks. Algebraic and Geometric Methods in Discrete Mathematics, 155-188. American Mathematical Society, Providence 2017.
3. * Jiří Vomlel, Milan Studený: Graphical and Algebraic Representatives of Conditional Independence Models. Advances in Probabilistic Graphical Models, 55-80. Springer Verlag, Berlin Heildeberg 2007.
4. * Milan Studený: Probabilistic Conditional Independence Structures. Springer, London 2005.
5. * Milan Studený: Other approaches to the description of conditional independence structures. Oxford Statistical Science Series. 27. Highly Structured Stochastic Systems, 106-108. Oxford University Press, New York 2003.
6. * Milan Studený, Jiřina Vejnarová: On multiinformation function as a tool for measuring stochastic dependence. NATO Science Series. 89. Learning in Graphical Models, 261-297. Kluwer Academic, Dordrecht 1998.
7. * Milan Studený: Description of Conditional Independence Structures by Means of Imsets: A Connection with Product Formula Validity. Uncertainty in Intelligent Systems, 179-194. Elsevier, Amsterdam 1993.

Journal articles (43)
1. * T. Boege, J. H. Bolt, Milan Studený: Self-adhesivity in lattices of abstract conditional independence models. Discrete Applied Mathematics 361:1 (2025), 196-225. Elsevier.   Download
2. * Milan Studený, Václav Kratochvíl: Facets of the cone of exact games. Mathematical Methods of Operations Research 95:1 (2022), 35-80. Springer.   Download
3. * Milan Studený: Conditional independence structures over four discrete random variables revisited: conditional Ingleton inequalities. IEEE Transactions on Information Theory 67:11 (2021), 7030-7049. Institute of Electrical and Electronics Engineers.   Download
4. * Milan Studený, J. Cussens, Václav Kratochvíl: The dual polyhedron to the chordal graph polytope and the rebuttal of the chordal graph conjecture. International Journal of Approximate Reasoning 138:1 (2021), 188-203. Elsevier.   Download
5. * Milan Studený: Contribution of František Matúš to the research on conditional independence. Kybernetika 56:5 (2020), 850-874. Ústav teorie informace a automatizace AV ČR, v. v. i..   Download
6. * Tomáš Kroupa, Milan Studený: Facets of the cone of totally balanced games. Mathematical Methods of Operations Research 90:2 (2019), 271-300. Springer.   Download
7. * Milan Studený, Václav Kratochvíl: Linear criterion for testing the extremity of an exact game based on its finest min-representation. International Journal of Approximate Reasoning 101:1 (2018), 49-68. Elsevier.   Download
8. * Milan Studený, J. Cussens: Towards using the chordal graph polytope in learning decomposable models. International Journal of Approximate Reasoning 88:1 (2017), 259-281. Elsevier.   Download
9. * J. Cussens, D. Haws, Milan Studený: Polyhedral aspects of score equivalence in Bayesian network structure learning. Mathematical Programming 164, 285-324. Springer.   Download
10. * Milan Studený, Tomáš Kroupa: Core-based criterion for extreme supermodular functions. Discrete Applied Mathematics 206:1 (2016), 122-151. Elsevier.   Download
11. * K. Tanaka, Milan Studený, A. Takemura, T. Sei: A linear-algebraic tool for conditional independence inference. Journal of Algebraic Statistics 6:2 (2015), 150-167.   Download
12. * Milan Studený, D. Haws: Learning Bayesian network structure: towards the essential graph by integer linear programming tools. International Journal of Approximate Reasoning 55:4 (2014), 1043-1071. Elsevier.   Download
13. * Milan Studený, D.C. Haws: On polyhedral approximations of polytopes for learning Bayesian networks. Journal of Algebraic Statistics 4:1 (2013), 59-92.   Download
14. * R. Hemmecke, S. Lindner, Milan Studený: Characteristic imsets for learning Bayesian network structure. International Journal of Approximate Reasoning 53:9 (2012), 1336-1349. Elsevier.   Download
15. * Milan Studený, Jiří Vomlel: On open questions in the geometric approach to structural learning Bayesian nets. International Journal of Approximate Reasoning 52:5 (2011), 627-640. Elsevier.   Download
16. * R. Bouckaert, R. Hemmecke, S. Lindner, Milan Studený: Efficient algorithms for conditional independence inference. Journal of Machine Learning Research 11:1 (2010), 3453-3479. Microtome Publ.   Download
17. * Milan Studený, Jiří Vomlel, R. Hemmecke: A geometric view on learning Bayesian network structures. International Journal of Approximate Reasoning 51:5 (2010), 578-586. Elsevier.   Download
18. * Milan Studený, A. Roverato, Š. Štěpánová: Two operations of merging and splitting components in a chain graph. Kybernetika 45:2 (2009), 208-248. Ústav teorie informace a automatizace AV ČR, v. v. i..   Download
19. * Milan Studený, Jiří Vomlel: A reconstruction algorithm for the essential graph. International Journal of Approximate Reasoning 50:2 (2009), 385-413. Elsevier.   Download
20. * A. Perez, Milan Studený: Comparison of two methods for approximation of probability distributions with prescribed marginals. Kybernetika 43:5 (2007), 591-618. Ústav teorie informace a automatizace AV ČR, v. v. i..   Download
21. * R. R. Bouckaert, Milan Studený: Racing algorithms for conditional independence inference. International Journal of Approximate Reasoning 45:2 (2007), 386-401. Elsevier.   Download
22. * A. Roverato, Milan Studený: A graphical representation of equivalence classes of AMP chain graphs. Journal of Machine Learning Research 7:6 (2006), 1045-1078. Microtome Publ.   Download
23. * Milan Studený: Characterization of inclusion neighbourhood in terms of the essential graph. International Journal of Approximate Reasoning 38:3 (2005), 283-309. Elsevier.   Download
24. * Milan Studený: Characterization of essential graphs by means of the operation of legal merging of components. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems 12, 43-62. World Scientific Publishing.
25. * Milan Studený: Chain graph models and their causal interpretations - discussion on the paper by Lauritzen and Richardson. Journal of the Royal Statistical Society Series B-Statistical Methodology 64:3 (2002), 358.
26. * Milan Studený: On stochastic conditional independence: the problems of characterization and description. Annals of Mathematics and Artificial Intelligence 35, 323-341.
27. * A. Paz, R. Y. Geva, Milan Studený: Representation of irrelevance relations by annotated graphs. Fundamenta Informaticae 42:1 (2000), 149-199. IOS Press.
28. * M. Volf, Milan Studený: A graphical characterization of the largest chain graphs. International Journal of Approximate Reasoning 20:3 (1999), 209-236. Elsevier.
29. * Milan Studený, R. R. Bouckaert: On chain graph models for description of conditional independence structures. Annals of Statistics 26:4 (1998), 1434-1495.
30. * Milan Studený: A recovery algorithm for chain graphs. International Journal of Approximate Reasoning 17:213 (1997), 265-293. Elsevier.
31. * Milan Studený: Semigraphoids and structures of probabilistic conditional independence. Annals of Mathematics and Artificial Intelligence 21:1 (1997), 71-98.
32. * Jana Zvárová, Milan Studený: Information-theoretic Approach to Constitution and Reduction of Medical Data. International Journal of Medical Informatics 45, 65-74. Elsevier.
33. * František Matúš, Milan Studený: Conditional independences among four random variables I. Combinatorics, Probability and Computing 4, 269-278.
34. * Milan Studený: Description of structures of stochastic conditional independence by means of faces and imsets. 2nd part: basic theory. . International Journal of General Systems 23:3 (1995), 201-219. Taylor & Francis.
35. * Milan Studený: Description of structures of stochastic conditional independence by means of faces and imsets. 3rd part: examples of use and appendices. International Journal of General Systems 23:4 (1995), 323-341. Taylor & Francis.
36. * Milan Studený: Conditional independence and natural conditional functions. International Journal of Approximate Reasoning 12:1 (1995), 43-68. Elsevier.
37. * Milan Studený: Description of structures of stochastic conditional independence by means of faces and imsets. 1st part: introduction and basic concepts. International Journal of General Systems 23:2 (1994), 123-137. Taylor & Francis.
38. * Milan Studený: Structural semigraphoids. International Journal of General Systems 22:2 (1994), 207-217. Taylor & Francis.
39. * Petr Lachout, Milan Studený, Jan Šindelář: On set-valued measures. Informatica 4, 21-44.
40. * Milan Studený: Convex Cones in Finite-Dimensional Real Vector Spaces. Kybernetika 29:2 (1993), 180-200. Ústav teorie informace a automatizace AV ČR, v. v. i..
41. * F. M. Malvestuto, Milan Studený: Comment on "A Unique Formal System for Binary Decompositions of Database Relations, Probability Distributions, and Graphs". Information Sciences 63, 1-2. Elsevier.
42. * Milan Studený: Multiinformation and the Problem of Characterization of Conditional Independence Relations. Problems of Control and Information Theory 18:1 (1989), 3-16.
43. * Milan Studený: Attempts at Axiomatic Description of Conditional Independence. Kybernetika 25:3 (1989), 72-79.

Conference papers (46)
1. * Milan Studený, J. Cussens, Václav Kratochvíl: Dual formulation of the chordal graph conjecture. Proceedings of Machine Learning Research, Volume 138: International Conference on Probabilistic Graphical Models, 23-25 September 2020, Hotel Comwell Rebild Bakker, Skørping, Denmark, 449-460. JMLR, Inc. and Microtome Publishing, Brookline 2021.   Download
2. * Milan Studený, Václav Kratochvíl, Jiří Vomlel: On irreducible min-balanced set systems. Symbolic and Quantitative Approaches to Reasoning with Uncertainty : 15th European Conference, ECSQARU 2019, Belgrade, Serbia, September 18-20, 2019, 444-454. Springer Intenational Publishing, Cham 2019.   Download
3. * Milan Studený, Tomáš Kroupa, Václav Kratochvíl: On attempts to characterize facet-defining inequalities of the cone of exact games. Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18), 177-187. MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University, Praha 2018.   Download
4. * Milan Studený, Václav Kratochvíl: Linear core-based criterion for testing extreme exact games. Proceedings of the 10th International Symposium on Imprecise Probability: Theories and Applications, 313-324. PMLR, Lugano 2017.   Download
5. * Milan Studený, J. Cussens: The chordal graph polytope for learning decomposable models. Proceedings of the Eighth International Conference on Probabilistic Graphical Models, 499-510. Microtome Publishing, Brookline 2016.   Download
6. * Milan Studený: How matroids occur in the context of learning Bayesian network structure. Uncertainty in Artificial Intelligence, Proceedings of the Thirty-First Conference (2015), 832-841. AUAI Press, Corvallis, Oregon 2015.   Download
7. * Milan Studený: Integer linear programming approach to learning Bayesian network structure: towards the essential graph. Proceedings of the 6th European Workshop on Graphical Models, 307-314. DESCAI, University of Granada, Granada 2012.   Download
8. * Milan Studený, D. Haws, R. Hemmecke, S. Lindner: Polyhedral approach to statistical learning graphical models. Harmony of Gröbner Bases and the Modern Industrial Society, 346-372. World Scientific Press, Singapore 2012.   Download
9. * Milan Studený, R. Hemmecke, S. Lindner: Characteristic imset: a simple algebraic representative of a Bayesian network structure. Proceedings of the 5th European Workshop on Probabilistic Graphical Models (PGM 2010), 257-264. HIIT Publications, Helsinki 2010.   Download
10. * Milan Studený, Jiří Vomlel: On open questions in the geometric approach to learning BN structures. WUPES'09, 226-236. University of Economics Prague, Praha 2009.   Download
11. * Milan Studený, Jiří Vomlel: A Geometric Approach to Learning BN Structures. Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM'08), 281-288. Aalborg University, Aalborg 2008.   Download
12. * Radim Jiroušek, Václav Kratochvíl, Tomáš Kroupa, Radim Lněnička, Milan Studený, Jiří Vomlel, P. Hampl, H. Hamplová: An evaluation of string similarity measures on pricelists of computer components. Proceedings of Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /10./, 1-6. UTIA AV ČR, Praha 2007.
13. * Jiří Vomlel, Milan Studený: Using imsets for learning Bayesian networks. Proceedings of Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty /10./, 178-189. UTIA AV ČR, Praha 2007.
14. * Milan Studený: An algeraic approach to structural learning Bayesian networks. IPMU 2006. Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2284-2291. Editions EDK, Paris 2006.
15. * H. Hamplová, J. Ivánek, Radim Jiroušek, Tomáš Kroupa, Radim Lněnička, Milan Studený, Jiří Vomlel: Decision support system for comparison of price lists. Proceedings of the 8th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, 32-38. Oeconomica, Praha 2005.
16. * R. R. Bouckaert, Milan Studený: Racing for conditional independence inference. Proceedings of the 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty 3571, 221-232.
17. * P. Šimeček, Milan Studený: Využití pojmu Hilbertovy báze pro ověřování hypotézy o shodnosti strukturálních a kombinatorických imsetů. Sborník prací 13. letní školy JČMF ROBUST 2004, 395-401. JČMF, Praha 2004.
18. * Milan Studený, Jiří Vomlel: Transition between graphical and algebraic representatives of Bayesian network models. Proceedings of the Second European Workshop on Probabilistic Graphical Models, 193-200. University of Nijmegen, Nijmegen 2004.
19. * Milan Studený: Structural imsets: an algebraic method for describing conditional independence structures. Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 1323-1330. Universita La Sapienza, Roma 2004.
20. * Petr Šimeček, Milan Studený: Využití Hilbertovy báze k ověření shodnosti strukturálních a kombinatorických imsetů. Sborník ROBUST 2004, 395-402. JČMF, Praha 2004.
21. * Milan Studený: Characterization of inclusion neighbourhood in terms of the essential graph: Lower neighbours. Proceedings of the 6th Workshop on Uncertainty Processing, 243-262. University of Economics, Prague 2003.
22. * Milan Studený: Characterization of inclusion neighbourhood in terms of the essential graph: upper neighbours. Symbolic and Quantitative Approaches to Reasoning with Uncertainty. European Conference, 161-172. Springer, Berlin 2003.
23. * Milan Studený: O použití řetězcových grafů pro popis struktur podmíněné nezávislosti. ROBUST'2002. Sborník prací dvanácté zimní školy JČMF, 292-314. JČMF, Praha 2002.
24. * Milan Studený: Characterization of essential graphs by means of an operation of legal component merging. Proceedings of the First European Workshop on Probabilistic Graphical Models, 161-168. University of Castilla, Cuenca 2002.
25. * Radim Jiroušek, Milan Studený, Jiřina Vejnarová: Open problems inspired by Albert Perez. Conditionals, Information, Inference, 117-128. Fern Universität, Hagen 2002.
26. * T. Kočka, R. R. Bouckaert, Milan Studený: On characterizing inclusion of Bayesian networks. Uncertainty in Artificial Intelligence. Proceeding of the 17th Conference, 261-268. Morgan Kaufmann, San Francisco 2001.
27. * Milan Studený: On stochastic conditional independence: Problem of characterization and description. Partial Knowledge and Uncertainty: Independence, Conditioning, Inference, 5-8. Baltzer Science Publ., Rome 2000.
28. * A. P. Dawid, Milan Studený: Conditional products: An alternative approach to conditional independence. Artificial Intelligence and Statistics 99. Proceedings, 32-40. Morgan Kaufmann, San Francisco 1999.
29. * Milan Studený: Complexity of structural models. Prague Stochastics '98. Proceedings, 521-528. JČMF, Praha 1998.
30. * Milan Studený: Bayesian networks from the point of view of chain graphs. Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference, 496-503. Morgan Kaufmann, San Francisco 1998.
31. * Milan Studený: Comparison of graphical approaches to description of conditional independence structures. Proceedings of the 4th Workshop on Uncertainty Processing, 156-172. VŠE, Praha 1997.
32. * Milan Studený: On marginalization, collapsibility and precollapsibility. Distributions with Given Marginals and Moment Problems, 191-198. Kluwer, Dordrecht 1997.
33. * Milan Studený: On separation criterion and recovery algorithm for chain graphs. Uncertainty in Artificial Intelligence. Proceedings, 509-516. Morgan Kaufmann Publ., San Francisco 1996.
34. * Milan Studený: On stochastic conditional independence structures. European Conference on Higly Structured Stochastic Systems. Proceedings, 165-169. University of Aalborg, Rebild 1996.
35. * Milan Studený: Marginal problem in different calculi of AI. Lecture Notes in Computer Science. 945. Advances in Intelligent Computing - IPMU '94, 348-359. Springer, Berlin 1995.
36. * R. R. Bouckaert, Milan Studený: Chain graphs: semantics and expressiveness. Lecture Notes in Artificial Intelligence. 946. Symbolic and Quantitative Approaches to Reasoning and Uncertainty, 67-76. Springer, Berlin 1995.
37. * Milan Studený, Pavel Boček: CI-models arising among 4 random variables. Uncertainty Processing in Expert Systems. Proceedings, 268-282. VŠE, Praha 1994.
38. * Milan Studený: Semigraphoids are two-antecedental approximations of stochastic conditional independence models. Uncertainty in Artificial Intelligence. Proceedings, 546-552. Morgan Kaufmann, San Francisco 1994.
39. * Milan Studený: Marginal problem in different calculi of AI. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Proceedings, 597-604. Cité Internationale Universitaire, Paris 1994.
40. * Milan Studený: Formal Properties of Conditional Independence in Different Calculi of AI. Lecture Notes in Computer Science. 747. Symbolic and Quantitative Approaches to Reasoning and Uncertainty, 341-348. Springer, Berlin 1993.
41. * Milan Studený, František Matúš, Jiřina Vejnarová: Decomposition of Large Systems and Independence Structures. Second European Congress on Systems Science, 891-898. Afcet, Paris 1993.
42. * Milan Studený: Popis struktur podmíněné stochastické nezávislosti pomocí formulí součinového typu. Sborník prací letní školy JČMF ROBUST '92, 146-155. JČMF, Praha 1992.
43. * Milan Studený: Description of Conditional Independence Structures by Means of Imsets: A Connection with Product Formula Validity. International Conference on Information Processing and Management of Uncertainty. IPMU '92, 503-506. Universitat des les Illes Balears, Palma 1992.
44. * Milan Studený: Conditional Independence Relations Have No Finite Complete Characterization. Transactions of the Eleventh Prague Conference on Information Theory, Statistical Decision Functions, Random Processes, 377-396. Academia, Prague 1992.
45. * Milan Studený: Convex Semigraphoids. Workshop on Uncertainty Processing in Expert Systems, -. ÚTIA ČSAV, Prague 1991.
46. * Milan Studený: Multiinformace jakožto nástroj pro studium podmíněné stochastické nezávislosti. PROBASTAT '89. Zborník príspevkov, 129. VVTŠ, Liptovský Mikuláš 1989.

Other publications (27)
1. * Milan Studený: On combinatorial descriptions of faces of the cone of supermodular functions. Research Report 2397. UTIA AV CR, Praha 2024.   Download
2. * Milan Studený: Basic facts concerning extreme supermodular functions. Research Report 2359. ÚTIA AV ČR v.v.i, Praha 2016.   Download
3. * Milan Studený: LP relaxations and pruning for characteristic imsets. Research Report 2323. ÚTIA AVČR, Praha 2012.   Download
4. * Milan Studený, D. Haws: On polyhedral approximations of polytopes for learning Bayes nets. Research Report 2303. ÚTIA AV ČR, Praha 2011.   Download
5. * Milan Studený, R. Hemmecke, Jiří Vomlel, S. Lindner: Polyhedral approach to statistical learning graphical models. Abstracts of The 2nd CREST-SBM International Conference on Harmony of Groebner Bases and the Moderm Industrial Socienty, 1-4. JST CREST, Osaka 2010.   Download
6. * Milan Studený: Mathematical aspects of learning Bayesian networks: Bayesian quality criteria. Research Report 2234. ÚTIA AV ČR, v.v.i, Praha 2008.   Download
7. * A. Perez, Milan Studený: Comparsion of Two Methods for Approximation of Probability Distributions with Prescribed Marginals. Interní publikace - DAR - ÚTIA 2005/39. ÚTIA AV ČR, Praha 2005.
8. * Milan Studený, A. Roverato, Š. Štěpánová: Two Operations of Merging Components in a Chain Graph. Interní publikace - DAR - ÚTIA 2005/38. ÚTIA AV ČR, Praha 2005.
9. * A. Roverato, Milan Studený: A Graphical Representation of Equivalence Classes of AMP Chain Graphs. Interní publikace - DAR - ÚTIA 2005/37. ÚTIA AV ČR, Praha 2005.
10. * Milan Studený: On methods of description of conditional independence structures. Abstract. Abstracts of the 24th European Meeting of Statisticians & 14th Prague Conference on Information Theory, Statistical Decision Functions and Random Processes, 333. Institute of Information Theory and Automation, Prague 2002.
11. * Milan Studený: Algebraic approach to learning Bayesian networks. Abstract. BAYESIAN STATISTICS 7 Programme Abstracts Participants, 179. Universitat de Valencia, Valencia 2002.
12. * Milan Studený: On non-graphical description of models of conditional independence structure. Katholieke Universiteit, Leuven 2001.   Download
14. * T. Kočka, R. R. Bouckaert, Milan Studený: On the Inclusion Problem. Research Report 2010. ÚTIA AV ČR, Praha 2001.
15. * Milan Studený, R. R. Bouckaert, T. Kočka: Extreme Supermodular Set Functions over Five Variables. Research Report 1977. ÚTIA AV ČR, Praha 2000.
16. * Osvaldo García-Mata, Milan Studený: About the Closure Operation for Relational Models Induced by Syntactic Inference Rules. Research Report 1959. ÚTIA AV ČR, Praha 1999.
17. * Milan Studený: On Recovery Algorithm for Chain Graphs. Research Report 1874. ÚTIA AV ČR, Praha 1996.
18. * Jana Zvárová, Milan Studený: Information theoretical approach to constitution and reduction of medical data. Abstract. EuroMISE '95: Information, Health and Education, 88. EuroMISE Center, Prague 1995.
19. * R. R. Bouckaert, Milan Studený: Chain Graphs: Semantics and Expressiveness - Extended Version. Research Report 1836. ÚTIA AV ČR, Praha 1995.
20. * Jana Zvárová, Karel Hrach, I. Malá, J. Peleška, Milan Studený, Martin Štefek, David Švejda, M. Tomečková: Managing Uncertainty in Medicine. Research Report . EuroMISE, Prague 1995.
21. * Jana Zvárová, Milan Studený: Information Theoretical Approach to Constitution and Reduction of Medical Data. EuroMISE 95: Information, Health and Education, 88. EuroMISE Center, Prague 1995.
22. * Milan Studený: Multiinformation and Conditional Independence II. ÚTIA ČSAV, Praha 1992.
23. * Petr Lachout, Milan Studený, Jan Šindelář: On Set-Valued Measures. ÚTIA ČSAV, Praha 1992.
24. * Milan Studený: Convex set Functions II. ÚTIA ČSAV, Praha 1991.
25. * Milan Studený: Convex Set Functions I. ÚTIA ČSAV, Praha 1991.
26. * Milan Studený: Convex cones in Rn. ÚTIA ČSAV, Praha 1991.
27. * Milan Studený: Multiinformation and Conditional Independence I. ÚTIA ČSAV, Praha 1989.

Miscellaneous (1)
1. * František Matúš, Milan Studený: Workshop on Conditional Independence Structures and Graphical Models. Book of Abstracts. ÚTIA AV ČR, Praha 1999.