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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) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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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