Newton, NJ (2012) 'An infinite-dimensional statistical manifold modelled on Hilbert space.' Journal of Functional Analysis, 263 (6). 1661 - 1681. ISSN 0022-1236

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Abstract

We construct an infinite-dimensional Hilbert manifold of probability measures on an abstract measurable space. The manifold, M, retains the first- and second-order features of finite-dimensional information geometry: the α-divergences admit first derivatives and mixed second derivatives, enabling the definition of the Fisher metric as a pseudo-Riemannian metric. This is enough for many applications; for example, it justifies certain projections of Markov processes onto finite-dimensional submanifolds in recursive estimation problems. M was constructed with the Fenchel-Legendre transform between Kullback-Leibler divergences, and its role in Bayesian estimation, in mind. This transform retains, on M, the symmetry of the finite-dimensional case. Many of the manifolds of finite-dimensional information geometry are shown to be C ∞-embedded submanifolds of M. In establishing this, we provide a framework in which many of the formal results of the finite-dimensional subject can be proved with full rigour. © 2012 Elsevier Inc.

Item Type:	Article
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QC Physics
Divisions:	Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User:	Jim Jamieson
Date Deposited:	05 Mar 2013 16:31
Last Modified:	23 Jan 2019 05:15
URI:	http://repository.essex.ac.uk/id/eprint/5538

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