Entropy 2014, 16(5), 2454-2471; https://doi.org/10.3390/e16052454
Computational Information Geometry in Statistics: Theory and Practice
1
Department of Mathematics and Statistics, The Open University, Walton Hall, Milton Keynes,Buckinghamshire MK7 6AA, UK
2
Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
*
Author to whom correspondence should be addressed.
Received: 27 March 2014 / Revised: 25 April 2014 / Accepted: 29 April 2014 / Published: 2 May 2014
(This article belongs to the Special Issue Information Geometry)
View Full-Text
|
Download PDF [735 KB, uploaded 24 February 2015]
Abstract
A broad view of the nature and potential of computational information geometry in statistics is offered. This new area suitably extends the manifold-based approach of classical information geometry to a simplicial setting, in order to obtain an operational universal model space. Additional underlying theory and illustrative real examples are presented. In the infinite-dimensional case, challenges inherent in this ambitious overall agenda are highlighted and promising new methodologies indicated. View Full-Text
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
Share & Cite This Article
MDPI and ACS Style
Critchley, F.; Marriott, P. Computational Information Geometry in Statistics: Theory and Practice. Entropy 2014, 16, 2454-2471.