Computational information geometry in statistics: mixture modelling

Karim Anaya-Izquierdo, Frank Critchley, Paul Marriott, Paul Vos

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

This paper applies the tools of computation information geometry – 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.
Original languageEnglish
Title of host publicationGeometric Science of Information
Subtitle of host publicationFirst International Conference, GSI 2013, Paris, France, August 28-30, 2013. Proceedings
EditorsFrank Nielsen, Frédéric Barbaresco
Place of PublicationBerlin, Germany
PublisherSpringer
Pages319-326
Number of pages8
VolumeIX
ISBN (Print)9783642400193
DOIs
Publication statusPublished - 2013

Publication series

NameLecture Notes in Computer Science
Volume8085

    Fingerprint

Cite this

Anaya-Izquierdo, K., Critchley, F., Marriott, P., & Vos, P. (2013). Computational information geometry in statistics: mixture modelling. In F. Nielsen, & F. Barbaresco (Eds.), Geometric Science of Information: First International Conference, GSI 2013, Paris, France, August 28-30, 2013. Proceedings (Vol. IX, pp. 319-326). (Lecture Notes in Computer Science; Vol. 8085). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-40020-9_34