919 resultados para Markov-switching
Resumo:
Mode of access: Internet.
Resumo:
"20 April 1984."
Resumo:
"8 September 1944."
Resumo:
"March 1984."
Resumo:
"March 1984"--Vol. 2.
Resumo:
"5 July 1983."
Resumo:
"5 July 1983."
Resumo:
"October 1968."
Resumo:
On cover: AD719413.
Resumo:
"NAVORD report 5922, NOLC report 413."
Resumo:
Thesis (Master's)--University of Washington, 2016-06
Resumo:
Thesis (Ph.D.)--University of Washington, 2016-06
Terrain classification based on markov random field texture modeling of SAR and SAR coherency images
Resumo:
This combined PET and ERP study was designed to identify the brain regions activated in switching and divided attention between different features of a single object using matched sensory stimuli and motor response. The ERP data have previously been reported in this journal [64]. We now present the corresponding PET data. We identified partially overlapping neural networks with paradigms requiring the switching or dividing of attention between the elements of complex visual stimuli. Regions of activation were found in the prefrontal and temporal cortices and cerebellum. Each task resulted in different prefrontal cortical regions of activation lending support to the functional subspecialisation of the prefrontal and temporal cortices being based on the cognitive operations required rather than the stimuli themselves. (C) 2003 Elsevier Science B.V. All rights reserved.
Resumo:
A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis-Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis.