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ORCIDMilan Studený
Person information
- affiliation: The Czech Academy of Sciences, Prague, Czech Republic
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2020 – today
- 2025
[j35]Tobias Boege, Janneke H. Bolt, Milan Studený:
Self-adhesivity in lattices of abstract conditional independence models. Discret. Appl. Math. 361: 196-225 (2025)- 2024
- [i9]Tobias Boege, Janneke H. Bolt, Milan Studený:
Self-adhesivity in lattices of abstract conditional independence models. CoRR abs/2402.14053 (2024) - 2022
- [j34]Milan Studený
, Václav Kratochvíl:
Facets of the cone of exact games. Math. Methods Oper. Res. 95(1): 35-80 (2022) - [i8]Phokion G. Kolaitis, Andrej E. Romashchenko, Milan Studený, Dan Suciu, Tobias Boege:
Algorithmic Aspects of Information Theory (Dagstuhl Seminar 22301). Dagstuhl Reports 12(7): 180-204 (2022) - 2021
- [j33]Milan Studený, James Cussens, Václav Kratochvíl:
The dual polyhedron to the chordal graph polytope and the rebuttal of the chordal graph conjecture. Int. J. Approx. Reason. 138: 188-203 (2021) - [j32]Milan Studený:
Conditional Independence Structures Over Four Discrete Random Variables Revisited: Conditional Ingleton Inequalities. IEEE Trans. Inf. Theory 67(11): 7030-7049 (2021) - [i7]Milan Studený, Václav Kratochvíl:
Facets of the cone of exact games. CoRR abs/2103.02414 (2021) - 2020
- [j31]Milan Studený:
Contribution of Frantisek Matus to the research on conditional independence. Kybernetika 56(5): 850-874 (2020) - [j30]Nihat Ay, Milan Studený:
Special issue in memory of František Matúš. Kybernetika 56(6): 1015-1021 (2020) - [c17]Milan Studený, James Cussens, Václav Kratochvíl:
Dual Formulation of the Chordal Graph Conjecture. PGM 2020: 449-460 - [i6]Milan Studený:
Conditional independence structures over four discrete random variables revisited: conditional Ingleton inequalities. CoRR abs/2012.04092 (2020)
2010 – 2019
- 2019
- [j29]Tomás Kroupa, Milan Studený:
Facets of the cone of totally balanced games. Math. Methods Oper. Res. 90(2): 271-300 (2019) - [c16]Milan Studený, Václav Kratochvíl, Jirí Vomlel:
On Irreducible Min-Balanced Set Systems. ECSQARU 2019: 444-454 - 2018
- [j28]Milan Studený, Václav Kratochvíl:
Linear criterion for testing the extremity of an exact game based on its finest min-representation. Int. J. Approx. Reason. 101: 49-68 (2018) - [c15]Václav Kratochvíl, Milan Studený:
Proceedings of the 9th International Conference on Probabilistic Graphical Models. PGM 2018: i-iv - [e2]Milan Studený, Václav Kratochvíl:
International Conference on Probabilistic Graphical Models, PGM 2018, 11-14 September 2018, Prague, Czech Republic. Proceedings of Machine Learning Research 72, PMLR 2018 [contents] - 2017
- [j27]Milan Studený, James Cussens:
Towards using the chordal graph polytope in learning decomposable models. Int. J. Approx. Reason. 88: 259-281 (2017) - [j26]James Cussens, David Haws, Milan Studený:
Polyhedral aspects of score equivalence in Bayesian network structure learning. Math. Program. 164(1-2): 285-324 (2017) - [c14]Milan Studený, Václav Kratochvíl:
Linear Core-Based Criterion for Testing Extreme Exact Games. ISIPTA 2017: 313-324 - 2016
- [j25]Milan Studený, Tomás Kroupa:
Core-based criterion for extreme supermodular functions. Discret. Appl. Math. 206: 122-151 (2016) - [c13]Milan Studený, James Cussens:
The Chordal Graph Polytope for Learning Decomposable Models. Probabilistic Graphical Models 2016: 499-510 - 2015
- [c12]Milan Studený:
How matroids occur in the context of learning Bayesian network structure. UAI 2015: 832-841 - 2014
- [j24]Milan Studený, David Haws:
Learning Bayesian network structure: Towards the essential graph by integer linear programming tools. Int. J. Approx. Reason. 55(4): 1043-1071 (2014) - 2013
- [i5]Tomas Kocka, Remco R. Bouckaert, Milan Studený:
On characterizing Inclusion of Bayesian Networks. CoRR abs/1301.2282 (2013) - [i4]Milan Studený:
Bayesian Networks from the Point of View of Chain Graphs. CoRR abs/1301.7414 (2013) - [i3]Milan Studený:
On Separation Criterion and Recovery Algorithm for Chain Graphs. CoRR abs/1302.3606 (2013) - [i2]Milan Studený:
Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models. CoRR abs/1302.6847 (2013) - 2012
- [j23]Raymond Hemmecke, Silvia Lindner, Milan Studený:
Characteristic imsets for learning Bayesian network structure. Int. J. Approx. Reason. 53(9): 1336-1349 (2012) - 2011
- [j22]Milan Studený, Jirí Vomlel:
On open questions in the geometric approach to structural learning Bayesian nets. Int. J. Approx. Reason. 52(5): 627-640 (2011) - 2010
- [j21]Milan Studený, Jirí Vomlel, Raymond Hemmecke:
A geometric view on learning Bayesian network structures. Int. J. Approx. Reason. 51(5): 573-586 (2010) - [j20]Remco R. Bouckaert, Raymond Hemmecke, Silvia Lindner, Milan Studený:
Efficient Algorithms for Conditional Independence Inference. J. Mach. Learn. Res. 11: 3453-3479 (2010) - [i1]Raymond Hemmecke, Silvia Lindner, Milan Studený:
Learning restricted Bayesian network structures. CoRR abs/1011.6664 (2010)
2000 – 2009
- 2009
- [j19]Milan Studený, Jirí Vomlel:
A reconstruction algorithm for the essential graph. Int. J. Approx. Reason. 50(2): 385-413 (2009) - [j18]Milan Studený, Alberto Roverato, Sárka Stepánová:
Two Operations of Merging and Splitting Components in a Chain Graph. Kybernetika 45(2): 208-248 (2009) - 2008
- [j17]Milan Studený, Jirí Vomlel:
Editorial Note. Int. J. Approx. Reason. 49(2): 344 (2008) - 2007
- [j16]Remco R. Bouckaert, Milan Studený:
Racing algorithms for conditional independence inference. Int. J. Approx. Reason. 45(2): 386-401 (2007) - [j15]Albert Perez, Milan Studený:
Comparison of two methods for approximation of probability distributions with prescribed marginals. Kybernetika 43(5): 591-618 (2007) - 2006
- [j14]Alberto Roverato, Milan Studený:
A Graphical Representation of Equivalence Classes of AMP Chain Graphs. J. Mach. Learn. Res. 7: 1045-1078 (2006) - [e1]Milan Studený, Jirí Vomlel:
Third European Workshop on Probabilistic Graphical Models, 12-15 September 2006, Prague, Czech Republic. Electronic Proceedings. 2006 [contents] - 2005
- [b1]Milan Studený:
Probabilistic conditional independence structures. Information science and statistics, Springer 2005, ISBN 978-1-85233-891-6, pp. I-XIV, 1-285 - [j13]Milan Studený:
Characterization of inclusion neighbourhood in terms of the essential graph. Int. J. Approx. Reason. 38(3): 283-309 (2005) - [c11]Remco R. Bouckaert, Milan Studený:
Racing for Conditional Independence Inference. ECSQARU 2005: 221-232 - 2004
- [j12]Milan Studený:
Characterization Of Essential Graphs By Means Of The Operation Of Legal Merging Of Components. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 12(Supplement-1): 43-62 (2004) - 2003
- [c10]Milan Studený:
Characterization of Inclusion Neighbourhood in Terms of the Essential Graph: Uper Neighbours. ECSQARU 2003: 161-172 - 2002
- [j11]Milan Studený:
On Stochastic Conditional Independence: the Problems of Characterization and Description. Ann. Math. Artif. Intell. 35(1-4): 323-341 (2002) - [c9]Milan Studený:
Characterization of Essential Graphs by Means of an Operation of Legal Component Merging. Probabilistic Graphical Models 2002 - 2001
- [c8]Tomás Kocka, Remco R. Bouckaert, Milan Studený:
On characterizing Inclusion of Bayesian Networks. UAI 2001: 261-268 - 2000
- [j10]Azaria Paz, Robert Y. Geva, Milan Studený:
Representation of Irrelevance Relations by Annotated Graphs. Fundam. Informaticae 42(2): 149-199 (2000)
1990 – 1999
- 1999
- [j9]Martin Volf, Milan Studený:
A graphical characterization of the largest chain graphs. Int. J. Approx. Reason. 20(3): 209-236 (1999) - [c7]A. Philip Dawid, Milan Studený:
Conditional products: An alternative approach to conditional independence. AISTATS 1999 - 1998
- [c6]Milan Studený:
Bayesian Networks from the Point of View of Chain Graphs. UAI 1998: 496-503 - [p1]Milan Studený, Jirina Vejnarová:
The Multiinformation Function as a Tool for Measuring Stochastic Dependence. Learning in Graphical Models 1998: 261-297 - 1997
- [j8]Milan Studený:
Semigraphoids and Structures of Probabilistic Conditional Independence. Ann. Math. Artif. Intell. 21(1): 71-98 (1997) - [j7]Milan Studený:
A recovery algorithm for chain graphs. Int. J. Approx. Reason. 17(2-3): 265-293 (1997) - 1996
- [c5]Milan Studený:
On Separation Criterion and Recovery Algorithm for Chain Graphs. UAI 1996: 509-516 - 1995
- [j6]Frantisek Matús, Milan Studený:
Conditional Independences among Four Random Variables 1. Comb. Probab. Comput. 4: 269-278 (1995) - [j5]Milan Studený:
Conditional independence and natural conditional functions. Int. J. Approx. Reason. 12(1): 43-68 (1995) - [c4]Remco R. Bouckaert, Milan Studený:
Chain graphs: semantics and expressiveness. ECSQARU 1995: 69-76 - 1994
- [c3]Milan Studený:
Marginal Problem in Different Calculi of AI. IPMU 1994: 348-359 - [c2]Milan Studený:
Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models. UAI 1994: 546-552 - 1993
- [j4]Milan Studený:
Convex cones in finite-dimensional real vector spaces. Kybernetika 29(2): 180-200 (1993) - [c1]Milan Studený:
Formal Properties of Conditional Independence in Different Calculi of AI. ECSQARU 1993: 341-348 - 1992
- [j3]Francesco M. Malvestuto, Milan Studený:
Comment on "A unique formal system for binary decompositions of database relations, probability distributions, and graphs". Inf. Sci. 63(1-2): 1-2 (1992)
1980 – 1989
- 1989
- [j2]Milan Studený:
Attempts at axiomatic description of conditional independence. Kybernetika 25(7): 72-79 (1989) - 1987
- [j1]Milan Studený:
Asymptotic behaviour of empirical multiinformation. Kybernetika 23(2): 124-135 (1987)
Coauthor Index
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