نتایج جستجو برای: variable sampling schemes

تعداد نتایج: 554660  

Journal: :EURASIP J. Adv. Sig. Proc. 2002
Yufei Huang Petar M. Djuric

Variable selection is very important in many fields, and for its resolution many procedures have been proposed and investigated. Among them are Bayesian methods that use Markov chain Monte-Carlo (MCMC) sampling algorithms. A problem with MCMC sampling, however, is that it cannot guarantee that the samples are exactly from the target distributions. This drawback is overcome by related methods kn...

2004
David J. Nott Peter J. Green

The need to explore model uncertainty in linear regression models with many predictors has motivated improvements in Markov chain Monte Carlo sampling algorithms for Bayesian variable selection. Traditional sampling algorithms for Bayesian variable selection may perform poorly when there are severe multicollinearities amongst the predictors. In this paper we describe a new sampling method based...

Journal: :NeuroImage 2008
Li-Wei Kuo Jyh-Horng Chen Van J. Wedeen Wen-Yih Isaac Tseng

Mapping complex crossing fibers using diffusion MRI techniques requires adequate angular precision and accuracy. Beyond diffusion tensor imaging (DTI), high angular resolution sampling schemes such as diffusion spectrum imaging (DSI) and q-ball imaging (QBI) were proposed to resolve crossing fibers. These schemes require hundreds of data approximately five to ten times more than DTI, offsetting...

2007
Robert E. McCulloch

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2012
Scott A. Mitchell Alexander Rand Mohamed S. Ebeida Chandrajit L. Bajaj

We introduce three natural and well-defined generalizations of maximal Poisson-disk sampling. The first is to decouple the disk-free (inhibition) radius from the maximality (coverage) radius. Selecting a smaller inhibition radius than the coverage radius yields samples which mix advantages of Poisson-disk and uniform-random samplings. The second generalization yields hierarchical samplings, by ...

Journal: :IEEE Trans. Systems, Man, and Cybernetics, Part A 2000
Thierry Denoeux

A new adaptive pattern classifier based on the Dempster–Shafer theory of evidence is presented. This method uses reference patterns as items of evidence regarding the class membership of each input pattern under consideration. This evidence is represented by basic belief assignments (BBA’s) and pooled using the Dempster’s rule of combination. This procedure can be implemented in a multilayer ne...

2014
Julián Salt Ángel Cuenca Francisco Palau Sebastián Dormido

In many control applications, the sensor technology used for the measurement of the variable to be controlled is not able to maintain a restricted sampling period. In this context, the assumption of regular and uniform sampling pattern is questionable. Moreover, if the control action updating can be faster than the output measurement frequency in order to fulfill the proposed closed loop behavi...

Journal: :Communications in Statistics - Theory and Methods 1986

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