Asymptotic behaviour of empirical multiinformation. - Milan Studený (Kybernetika, 1987)

Attempts at axiomatic description of conditional independence. - Milan Studený (Kybernetika, 1989)

Comment on "A unique formal system for binary decompositions of database relations, probability distributions, and graphs". - Francesco M. Malvestuto; Milan Studený (Inf. Sci., 1992)

Formal Properties of Conditional Independence in Different Calculi of AI. - Milan Studený (ECSQARU, 1993)

Convex cones in finite-dimensional real vector spaces. - Milan Studený (Kybernetika, 1993)

Marginal Problem in Different Calculi of AI. - Milan Studený (IPMU, 1994)

Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models. - Milan Studený (UAI, 1994)

Chain graphs: semantics and expressiveness. - Remco R. Bouckaert; Milan Studený (ECSQARU, 1995)

Conditional Independences among Four Random Variables 1. - Frantisek Matús; Milan Studený (Comb. Probab. Comput., 1995)

Conditional independence and natural conditional functions. - Milan Studený (Int. J. Approx. Reason., 1995)

On Separation Criterion and Recovery Algorithm for Chain Graphs. - Milan Studený (UAI, 1996)

Semigraphoids and Structures of Probabilistic Conditional Independence. - Milan Studený (Ann. Math. Artif. Intell., 1997)

A recovery algorithm for chain graphs. - Milan Studený (Int. J. Approx. Reason., 1997)

The Multiinformation Function as a Tool for Measuring Stochastic Dependence. - Milan Studený; Jirina Vejnarová (Learning in Graphical Models, 1998)

Bayesian Networks from the Point of View of Chain Graphs. - Milan Studený (UAI, 1998)

Conditional products: An alternative approach to conditional independence. - A. Philip Dawid; Milan Studený (AISTATS, 1999)

A graphical characterization of the largest chain graphs. - Martin Volf; Milan Studený (Int. J. Approx. Reason., 1999)

Representation of Irrelevance Relations by Annotated Graphs. - Azaria Paz; Robert Y. Geva; Milan Studený (Fundam. Informaticae, 2000)

On characterizing Inclusion of Bayesian Networks. - Tomás Kocka; Remco R. Bouckaert; Milan Studený (UAI, 2001)

Characterization of Essential Graphs by Means of an Operation of Legal Component Merging. - Milan Studený (Probabilistic Graphical Models, 2002)

On Stochastic Conditional Independence: the Problems of Characterization and Description. - Milan Studený (Ann. Math. Artif. Intell., 2002)

Characterization of Inclusion Neighbourhood in Terms of the Essential Graph: Uper Neighbours. - Milan Studený (ECSQARU, 2003)

Characterization Of Essential Graphs By Means Of The Operation Of Legal Merging Of Components. - Milan Studený (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2004)

Probabilistic conditional independence structures. - Milan Studený (Springer, 2005)

Racing for Conditional Independence Inference. - Remco R. Bouckaert; Milan Studený (ECSQARU, 2005)

Characterization of inclusion neighbourhood in terms of the essential graph. - Milan Studený (Int. J. Approx. Reason., 2005)

A Graphical Representation of Equivalence Classes of AMP Chain Graphs. - Alberto Roverato; Milan Studený (J. Mach. Learn. Res., 2006)

Racing algorithms for conditional independence inference. - Remco R. Bouckaert; Milan Studený (Int. J. Approx. Reason., 2007)

Comparison of two methods for approximation of probability distributions with prescribed marginals. - Albert Perez; Milan Studený (Kybernetika, 2007)

Editorial Note. - Milan Studený; Jirí Vomlel (Int. J. Approx. Reason., 2008)

A reconstruction algorithm for the essential graph. - Milan Studený; Jirí Vomlel (Int. J. Approx. Reason., 2009)

Two Operations of Merging and Splitting Components in a Chain Graph. - Milan Studený; Alberto Roverato; Sárka Stepánová (Kybernetika, 2009)

Learning restricted Bayesian network structures - Raymond Hemmecke; Silvia Lindner; Milan Studený (CoRR, 2010)

A geometric view on learning Bayesian network structures. - Milan Studený; Jirí Vomlel; Raymond Hemmecke (Int. J. Approx. Reason., 2010)

Efficient Algorithms for Conditional Independence Inference. - Remco R. Bouckaert; Raymond Hemmecke; Silvia Lindner; Milan Studený (J. Mach. Learn. Res., 2010)

On open questions in the geometric approach to structural learning Bayesian nets. - Milan Studený; Jirí Vomlel (Int. J. Approx. Reason., 2011)

Characteristic imsets for learning Bayesian network structure. - Raymond Hemmecke; Silvia Lindner; Milan Studený (Int. J. Approx. Reason., 2012)

Learning Bayesian network structure: Towards the essential graph by integer linear programming tools. - Milan Studený; David Haws (Int. J. Approx. Reason., 2014)

How matroids occur in the context of learning Bayesian network structure. - Milan Studený (UAI, 2015)

The Chordal Graph Polytope for Learning Decomposable Models. - Milan Studený; James Cussens (Probabilistic Graphical Models, 2016)

Core-based criterion for extreme supermodular functions. - Milan Studený; Tomás Kroupa (Discret. Appl. Math., 2016)

Linear Core-Based Criterion for Testing Extreme Exact Games. - Milan Studený; Václav Kratochvíl (ISIPTA, 2017)

Towards using the chordal graph polytope in learning decomposable models. - Milan Studený; James Cussens (Int. J. Approx. Reason., 2017)

Polyhedral aspects of score equivalence in Bayesian network structure learning. - James Cussens; David Haws; Milan Studený (Math. Program., 2017)

Proceedings of the 9th International Conference on Probabilistic Graphical Models. - Václav Kratochvíl; Milan Studený (PGM, 2018)

Linear criterion for testing the extremity of an exact game based on its finest min-representation. - Milan Studený; Václav Kratochvíl (Int. J. Approx. Reason., 2018)

On Irreducible Min-Balanced Set Systems. - Milan Studený; Václav Kratochvíl; Jirí Vomlel (ECSQARU, 2019)

Facets of the cone of totally balanced games. - Tomás Kroupa; Milan Studený (Math. Methods Oper. Res., 2019)

Dual Formulation of the Chordal Graph Conjecture. - Milan Studený; James Cussens; Václav Kratochvíl (PGM, 2020)

Special issue in memory of František Matúš. - Nihat Ay; Milan Studený (Kybernetika, 2020)

Contribution of Frantisek Matus to the research on conditional independence. - Milan Studený (Kybernetika, 2020)

The dual polyhedron to the chordal graph polytope and the rebuttal of the chordal graph conjecture. - Milan Studený; James Cussens; Václav Kratochvíl (Int. J. Approx. Reason., 2021)

Conditional Independence Structures Over Four Discrete Random Variables Revisited: Conditional Ingleton Inequalities. - Milan Studený (IEEE Trans. Inf. Theory, 2021)

Facets of the cone of exact games. - Milan Studený; Václav Kratochvíl (Math. Methods Oper. Res., 2022)

Algorithmic Aspects of Information Theory (Dagstuhl Seminar 22301). - Phokion G. Kolaitis; Andrei E. Romashchenko; Milan Studený; Dan Suciu; Tobias Boege (Dagstuhl Reports, 2022)

On combinatorial descriptions of faces of the cone of supermodular functions. - Milan Studený (CoRR, 2024)

Self-adhesivity in lattices of abstract conditional independence models. - Tobias Boege; Janneke H. Bolt; Milan Studený (Discret. Appl. Math., 2025)