--- res: bibo_abstract: - The concepts of faithfulness and strong-faithfulness are important for statistical learning of graphical models. Graphs are not sufficient for describing the association structure of a discrete distribution. Hypergraphs representing hierarchical log-linear models are considered instead, and the concept of parametric (strong-) faithfulness with respect to a hypergraph is introduced. Strong-faithfulness ensures the existence of uniformly consistent parameter estimators and enables building uniformly consistent procedures for a hypergraph search. The strength of association in a discrete distribution can be quantified with various measures, leading to different concepts of strong-faithfulness. Lower and upper bounds for the proportions of distributions that do not satisfy strong-faithfulness are computed for different parameterizations and measures of association.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Anna foaf_name: Klimova, Anna foaf_surname: Klimova foaf_workInfoHomepage: http://www.librecat.org/personId=31934120-F248-11E8-B48F-1D18A9856A87 - foaf_Person: foaf_givenName: Caroline foaf_name: Uhler, Caroline foaf_surname: Uhler foaf_workInfoHomepage: http://www.librecat.org/personId=49ADD78E-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-7008-0216 - foaf_Person: foaf_givenName: Tamás foaf_name: Rudas, Tamás foaf_surname: Rudas bibo_doi: 10.1016/j.csda.2015.01.017 bibo_issue: '7' bibo_volume: 87 dct_date: 2015^xs_gYear dct_language: eng dct_publisher: Elsevier@ dct_title: Faithfulness and learning hypergraphs from discrete distributions@ ...