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dc.contributor.authorCasanellas, Marta
dc.contributor.authorPetrović, Sonja
dc.contributor.authorUhler, Caroline
dc.date.accessioned2022-06-28T19:08:58Z
dc.date.available2021-10-27T20:34:30Z
dc.date.available2022-06-28T19:08:58Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/1721.1/136249.2
dc.description.abstract© 2020 Annual Review of Statistics and Its Application. All rights reserved. Algebraic statistics uses tools from algebra (especially from multilinear algebra, commutative algebra, and computational algebra), geometry, and combinatorics to provide insight into knotty problems in mathematical statistics. In this review, we illustrate this on three problems related to networks: network models for relational data, causal structure discovery, and phylogenetics. For each problem, we give an overview of recent results in algebraic statistics, with emphasis on the statistical achievements made possible by these tools and their practical relevance for applications to other scientific disciplines.en_US
dc.language.isoen
dc.publisherAnnual Reviewsen_US
dc.relation.isversionof10.1146/ANNUREV-STATISTICS-031017-100053en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleAlgebraic Statistics in Practice: Applications to Networksen_US
dc.typeArticleen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalAnnual Review of Statistics and Its Applicationen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-04-12T17:19:15Z
dspace.orderedauthorsCasanellas, M; Petrović, S; Uhler, Cen_US
dspace.date.submission2021-04-12T17:19:16Z
mit.journal.volume7en_US
mit.journal.issue1en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusPublication Information Neededen_US


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