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Computational information geometry in statistics: mixture modelling

Anaya Izquierdo, Karim; Critchley, Frank; Marriott, Paul and Vos, Paul (2013). Computational information geometry in statistics: mixture modelling. In: Geometric Science of Information, 28-30 Aug 2013, École des Mines, Paris.

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URL: http://www.see.asso.fr/node/4339
DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-642-40020-9
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Abstract

This paper applies the tools of computation information geometry [3] – in particular, high dimensional extended multinomial families as proxies for the ‘space of all distributions’ – in the inferentially demanding area of statistical mixture modelling. A range of resultant benefits are noted.

Item Type: Conference or Workshop Item
Copyright Holders: 2013 Springer-Verlag
Extra Information: Geometric Science of Information
First International Conference, GSI 2013, Paris, France, August 28-30, 2013. Proceedings
Eds. Frank Nielsen, Frédéric Barbaresco
Lecture Notes in Computer Science, 8085
ISBN: 978-3-642-40019-3
pp.319-326
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
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Item ID: 38382
Depositing User: Frank Critchley
Date Deposited: 11 Sep 2013 11:43
Last Modified: 08 Dec 2018 20:34
URI: http://oro.open.ac.uk/id/eprint/38382
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