Results 31 to 40 of about 70 (70)

Markov Kernels and the Conditional Extreme Value Model [PDF]

, 2012
Abstract : The classical approach to extreme value modelling for multivariate data is to assume that the joint distribution belongs to a multivariate domain of attraction. In particular, this requires that each marginal distribution be individually attracted to a univariate extreme value distribution.
David Zeber, Sidney I. Resnick
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Cubature Formulas, Geometrical Designs, Reproducing Kernels, and Markov Operators

, 2005
Cubature formulas and geometrical designs are described in terms of reproducing kernels for Hilbert spaces of functions on the one hand, and Markov operators associated to orthogonal group representations on the other hand. In this way, several known results for spheres in Euclidean spaces, involving cubature formulas for polynomial functions and ...
De La Harpe, Pierre, Pache, Claude
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Markov kernels in Mathlib's probability library

28 ...
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Reversible Markov kernels and involutions on product spaces

Latin American Journal of Probability and Mathematical Statistics
In this paper the relations between independence preserving (IP) involutions and reversible Markov kernels are investigated. We introduce an involutive augmentation H = (f, g_f) of a measurable function f and relate the IP property of H to f-generated reversible Markov kernels.
Piccioni, Mauro, Wesołowski, Jacek
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Potential kernels for recurrent Markov chains

Journal of Mathematical Analysis and Applications, 1964
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Markov kernels and tribes

Information Sciences, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On contraction properties of Markov kernels

Probability Theory and Related Fields, 2003
The authors study general properties of contractions of Markov kernels without assumptions on the existence of an invariant probability measure. In their previous paper [in: Séminaire de Probabilités XXXIV. Lect. Notes Math. 1729, 1-145 (2000; Zbl 0963.60040)] they have shown that the system is forgetting its initialization without nevertheless ...
Del Moral, P., Ledoux, M., Miclo, L.
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Weak Convergence of Markov Kernels

2015
As indicated in the previous chapter, stable convergence of random variables can be seen as suitable convergence of Markov kernels given by conditional distributions. The required facts from the theory of weak convergence of Markov kernels will be presented in this chapter.
Erich Häusler, Harald Luschgy
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Spatial Markov Kernels for Image Categorization and Annotation

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011
This paper presents a novel discriminative stochastic method for image categorization and annotation. We first divide the images into blocks on a regular grid and then generate visual keywords through quantizing the features of image blocks. The traditional Markov chain model is generalized to capture 2-D spatial dependence between visual keywords by ...
, Zhiwu Lu, H H S, Ip
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Aspects of large random Markov kernels

Stochastics, 2009
We briefly review certain asymptotic properties of random Markov kernels on a finite state space. These models can be thought of as finite Markov chains in random environment. Here, the asymptotics are taken with respect to the cardinality of the state space. We study, for instance, the behaviour of the normalized invariant vector, the global behaviour
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