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.

DOI: https://doi.org/10.1007/978-3-642-40020-9

URL: http://www.see.asso.fr/node/4339

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.

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• Item ORO ID
• 38382
• Item Type
• Conference or Workshop Item
• 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 or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2013 Springer-Verlag
• Related URLs
• • http://www.gsi2013.org/(Other)
• Depositing User
• Frank Critchley