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Information geometric similarity measurement for near-random stochastic processes

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Information geometric similarity measurement for near-random stochastic processes

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Título Information geometric similarity measurement for near-random stochastic processes
Autor Dodson, C.T.J.
Scharcanski, Jacob
Abstract We outline the information-theoretic differential geometry of gamma distributions, which contain exponential distributions as a special case, and log-gamma distributions. Our arguments support the opinion that these distributions have a natural role in representing departures from randomness, uniformity, and Gaussian behavior in stochastic processes. We show also how the information geometry provides a surprisingly tractable Riemannian manifold and product spaces thereof, on which may be represented the evolution of a stochastic process, or the comparison of different processes, by means of well-founded maximum likelihood parameter estimation. Our model incorporates possible correlations among parameters. We discuss applications and provide some illustrations from a recent study of amino acid self-clustering in protein sequences; we provide also some results from simulations for multisymbol sequences.
Contido em IEEE transactions on systems, man, and cybernetics. A, Systems and humans. New York. Vol. 33, No. 4 (2003), p. 435-440
Assunto Matemática
[en] Gamma models
[en] Information geometry
[en] Multisymbol sequences
[en] Random
[en] Search
[en] Stochastic process
Origem Estrangeiro
Tipo Artigo de periódico
URI http://hdl.handle.net/10183/27592
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