4 papers
Marginal Models: an Overview
Tamas Rudas, Wicher Bergsma
Marginal models involve restrictions on the conditional and marginal association structure of a set of categorical variables. They generalize log-linear models for contingency tabl…
On the closure of relational models
Anna Klimova, Tamás Rudas
Relational models for contingency tables are generalizations of log-linear models, allowing effects associated with arbitrary subsets of cells in a possibly incomplete table, and n…
Directionally collapsible parameterizations of multivariate binary distributions
Tamas Rudas
Odds ratios and log-linear parameters are not collapsible, meaning that including a variable into the analysis or omitting one from it, may change the strength of association among…
Faithfulness and learning hypergraphs from discrete distributions
Anna Klimova, Caroline Uhler, Tamas Rudas
The concepts of faithfulness and strong-faithfulness are important for statistical learning of graphical models. Graphs are not sufficient for describing the association structure…