Algebraic statistics in practice: applications to networks
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...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/344004 |
| Acceso en línea: | https://hdl.handle.net/2117/344004 https://dx.doi.org/10.1146/annurev-statistics-031017-100053 |
| Access Level: | acceso abierto |
| Palabra clave: | Algebraic statistics Network models Graphical models Causal structure discovery Phylogenetics Classificació AMS::08 General algebraic systems Àrees temàtiques de la UPC::Matemàtiques i estadística |
| Sumario: | 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. |
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