{"scopus_import":1,"date_updated":"2021-01-12T06:54:43Z","month":"07","author":[{"first_name":"Anna","id":"31934120-F248-11E8-B48F-1D18A9856A87","last_name":"Klimova","full_name":"Klimova, Anna"},{"first_name":"Caroline","full_name":"Uhler, Caroline","last_name":"Uhler","id":"49ADD78E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-7008-0216"},{"first_name":"Tamás","last_name":"Rudas","full_name":"Rudas, Tamás"}],"title":"Faithfulness and learning hypergraphs from discrete distributions","doi":"10.1016/j.csda.2015.01.017","_id":"2014","volume":87,"citation":{"chicago":"Klimova, Anna, Caroline Uhler, and Tamás Rudas. “Faithfulness and Learning Hypergraphs from Discrete Distributions.” <i>Computational Statistics &#38; Data Analysis</i>. Elsevier, 2015. <a href=\"https://doi.org/10.1016/j.csda.2015.01.017\">https://doi.org/10.1016/j.csda.2015.01.017</a>.","apa":"Klimova, A., Uhler, C., &#38; Rudas, T. (2015). Faithfulness and learning hypergraphs from discrete distributions. <i>Computational Statistics &#38; Data Analysis</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.csda.2015.01.017\">https://doi.org/10.1016/j.csda.2015.01.017</a>","ieee":"A. Klimova, C. Uhler, and T. Rudas, “Faithfulness and learning hypergraphs from discrete distributions,” <i>Computational Statistics &#38; Data Analysis</i>, vol. 87, no. 7. Elsevier, pp. 57–72, 2015.","ama":"Klimova A, Uhler C, Rudas T. Faithfulness and learning hypergraphs from discrete distributions. <i>Computational Statistics &#38; Data Analysis</i>. 2015;87(7):57-72. doi:<a href=\"https://doi.org/10.1016/j.csda.2015.01.017\">10.1016/j.csda.2015.01.017</a>","ista":"Klimova A, Uhler C, Rudas T. 2015. Faithfulness and learning hypergraphs from discrete distributions. Computational Statistics &#38; Data Analysis. 87(7), 57–72.","short":"A. Klimova, C. Uhler, T. Rudas, Computational Statistics &#38; Data Analysis 87 (2015) 57–72.","mla":"Klimova, Anna, et al. “Faithfulness and Learning Hypergraphs from Discrete Distributions.” <i>Computational Statistics &#38; Data Analysis</i>, vol. 87, no. 7, Elsevier, 2015, pp. 57–72, doi:<a href=\"https://doi.org/10.1016/j.csda.2015.01.017\">10.1016/j.csda.2015.01.017</a>."},"type":"journal_article","date_published":"2015-07-01T00:00:00Z","date_created":"2018-12-11T11:55:13Z","day":"01","publication_status":"published","publication":"Computational Statistics & Data Analysis","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","page":"57 - 72","department":[{"_id":"CaUh"}],"oa":1,"abstract":[{"text":"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.","lang":"eng"}],"year":"2015","main_file_link":[{"url":"http://arxiv.org/abs/1404.6617","open_access":"1"}],"intvolume":"        87","publist_id":"5062","language":[{"iso":"eng"}],"issue":"7","quality_controlled":"1","publisher":"Elsevier"}