Information geometric similarity measurement for near-random stochastic processes

AUTOR(ES)

Dodson, C.T.J.

DATA DE PUBLICAÇÃO

2011

RESUMO

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.

ASSUNTO(S)

matemática gamma models information geometry multisymbol sequences random search stochastic process

Documentos Relacionados

• High Rates of Human Immunodeficiency Virus Type 1 Recombination: Near-Random Segregation of Markers One Kilobase Apart in One Round of Viral Replication
• WEAK TOPOLOGIES FOR STOCHASTIC PROCESSES*
• LIMIT THEOREMS FOR HOMOGENEOUS STOCHASTIC PROCESSES
• A TCHEBYCHEFF-LIKE INEQUALITY FOR STOCHASTIC PROCESSES*
• Coarse-grained stochastic processes for microscopic lattice systems