34 citations · 60 across the 5 of their papers we have counts for
5 papers
Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models
Milan Studeny
The semigraphoid closure of every couple of CI-statements (GI=conditional independence) is a stochastic CI-model. As a consequence of this result it is shown that every probabilist…
On Separation Criterion and Recovery Algorithm for Chain Graphs
Milan Studeny
Chain graphs give a natural unifying point of view on Markov and Bayesian networks and enlarge the potential of graphical models for description of conditional independence structu…
Bayesian Networks from the Point of View of Chain Graphs
Milan Studeny
AThe paper gives a few arguments in favour of the use of chain graphs for description of probabilistic conditional independence structures. Every Bayesian network model can be equi…
On characterizing Inclusion of Bayesian Networks
Tomas Kocka, Remco R. Bouckaert, Milan Studeny
Every directed acyclic graph (DAG) over a finite non-empty set of variables (= nodes) N induces an independence model over N, which is a list of conditional independence statements…
Learning restricted Bayesian network structures
Raymond Hemmecke, Silvia Lindner, Milan Studený
Bayesian networks are basic graphical models, used widely both in statistics and artificial intelligence. These statistical models of conditional independence structure are describ…