--- res: bibo_abstract: - "Various kinds of data are routinely represented as discrete probability distributions. Examples include text documents summarized by histograms of word occurrences and images represented as histograms of oriented gradients. Viewing a discrete probability distribution as a point in the standard simplex of the appropriate dimension, we can understand collections of such objects in geometric and topological terms. Importantly, instead of using the standard Euclidean distance, we look into dissimilarity measures with information-theoretic justification, and we develop the theory\r\nneeded for applying topological data analysis in this setting. In doing so, we emphasize constructions that enable the usage of existing computational topology software in this context.@eng" bibo_authorlist: - foaf_Person: foaf_givenName: Herbert foaf_name: Edelsbrunner, Herbert foaf_surname: Edelsbrunner foaf_workInfoHomepage: http://www.librecat.org/personId=3FB178DA-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-9823-6833 - foaf_Person: foaf_givenName: Ziga foaf_name: Virk, Ziga foaf_surname: Virk - foaf_Person: foaf_givenName: Hubert foaf_name: Wagner, Hubert foaf_surname: Wagner foaf_workInfoHomepage: http://www.librecat.org/personId=379CA8B8-F248-11E8-B48F-1D18A9856A87 bibo_doi: 10.4230/LIPICS.SOCG.2019.31 bibo_volume: 129 dct_date: 2019^xs_gYear dct_isPartOf: - http://id.crossref.org/issn/9783959771047 dct_language: eng dct_publisher: Schloss Dagstuhl - Leibniz-Zentrum für Informatik@ dct_title: Topological data analysis in information space@ ...