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Entropy 2014, 16(5), 2454-2471; doi: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)
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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.
Keywords: information geometry; computational geometry; statistical foundations information geometry; computational geometry; statistical foundations
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Critchley, F.; Marriott, P. Computational Information Geometry in Statistics: Theory and Practice. Entropy 2014, 16, 2454-2471.

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