Computational information geometry in statistics: mixture modelling

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

doi:10.1007/978-3-642-40020-9_34

Computational information geometry in statistics

mixture modelling

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

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English
Title of host publication	Geometric Science of Information
Subtitle of host publication	First International Conference, GSI 2013, Paris, France, August 28-30, 2013. Proceedings
Editors	Frank Nielsen, Frédéric Barbaresco
Place of Publication	Berlin, Germany
Publisher	Springer
Pages	319-326
Number of pages	8
Volume	IX
ISBN (Print)	9783642400193
DOIs	https://doi.org/10.1007/978-3-642-40020-9_34
Publication status	Published - 2013

Publication series

Name	Lecture Notes in Computer Science
Volume	8085

Fingerprint

Information Geometry

Mixture Modeling

Statistical Modeling

Computational Geometry

High-dimensional

Statistics

Range of data

Family

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

Computational information geometry in statistics : mixture modelling. / Anaya-Izquierdo, Karim; Critchley, Frank; Marriott, Paul; Vos, Paul.

Geometric Science of Information: First International Conference, GSI 2013, Paris, France, August 28-30, 2013. Proceedings. ed. / Frank Nielsen; Frédéric Barbaresco. Vol. IX Berlin, Germany : Springer, 2013. p. 319-326 (Lecture Notes in Computer Science; Vol. 8085).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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, Lecture Notes in Computer Science, vol. 8085, Springer, Berlin, Germany, pp. 319-326. https://doi.org/10.1007/978-3-642-40020-9_34

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

Anaya-Izquierdo, Karim ; Critchley, Frank ; Marriott, Paul ; Vos, Paul. / Computational information geometry in statistics : mixture modelling. Geometric Science of Information: First International Conference, GSI 2013, Paris, France, August 28-30, 2013. Proceedings. editor / Frank Nielsen ; Frédéric Barbaresco. Vol. IX Berlin, Germany : Springer, 2013. pp. 319-326 (Lecture Notes in Computer Science).

@inbook{ce6fd326bbba4676bf296b005111e1ea,

title = "Computational information geometry in statistics: mixture modelling",

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.",

author = "Karim Anaya-Izquierdo and Frank Critchley and Paul Marriott and Paul Vos",

year = "2013",

doi = "10.1007/978-3-642-40020-9_34",

language = "English",

isbn = "9783642400193",

volume = "IX",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "319--326",

editor = "Frank Nielsen and Fr{\'e}d{\'e}ric Barbaresco",

booktitle = "Geometric Science of Information",

}

TY - CHAP

T1 - Computational information geometry in statistics

T2 - mixture modelling

AU - Anaya-Izquierdo, Karim

AU - Critchley, Frank

AU - Marriott, Paul

AU - Vos, Paul

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

UR - http://dx.doi.org/10.1007/978-3-642-40020-9_34

U2 - 10.1007/978-3-642-40020-9_34

DO - 10.1007/978-3-642-40020-9_34

M3 - Chapter

SN - 9783642400193

VL - IX

T3 - Lecture Notes in Computer Science

SP - 319

EP - 326

BT - Geometric Science of Information

A2 - Nielsen, Frank

A2 - Barbaresco, Frédéric

PB - Springer

CY - Berlin, Germany

ER -

Access to Document

10.1007/978-3-642-40020-9_34