Algebraic Statistics in Practice: Applications to Networks
Name
1906.09537.pdf
Description
Submitted version
Size
3.6 MB
Format
Unknown
Checksum (MD5)
3e05eabc341a740583178b6a71290c15
Author(s)
Casanellas, Marta
Petrović, Sonja
Uhler, Caroline
Date Issued
2020
Journal
Annual Review of Statistics and Its Application
Publisher
Annual Reviews
Version
Original manuscript
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1146/ANNUREV-STATISTICS-031017-100053
