46 citations · 136 across the 5 of their papers we have counts for
5 papers
Improved learning of Bayesian networks
Tomas Kocka, Robert Castelo
The search space of Bayesian Network structures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of Bayes…
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…
Dimension Correction for Hierarchical Latent Class Models
Tomas Kocka, Nevin Lianwen Zhang
Model complexity is an important factor to consider when selecting among graphical models. When all variables are observed, the complexity of a model can be measured by its standar…
On Local Optima in Learning Bayesian Networks
Jens D. Nielsen, Tomas Kocka, Jose M. Pena
This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it…
Effective Dimensions of Hierarchical Latent Class Models
T. Kocka, N. L. Zhang
Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are latent. There are no theoretically well justifie…