Results 11 to 20 of about 70 (70)
Optimal measures and Markov transition kernels [PDF]
Journal of Global Optimization, 2012 We study optimal solutions to an abstract optimization problem for measures, which is a generalization of classical variational problems in information theory and statistical physics. In the classical problems, information and relative entropy are defined using the Kullback-Leibler divergence, and for this reason optimal measures belong to a one ...
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Asymptotics of Markov Kernels and the Tail Chain [PDF]
Advances in Applied Probability, 2013 An asymptotic model for the extreme behavior of certain Markov chains is the ‘tail chain’. Generally taking the form of a multiplicative random walk, it is useful in deriving extremal characteristics, such as point process limits. We place this model in a more general context, formulated in terms of extreme value theory for transition kernels, and ...
Resnick, Sidney I., Zeber, David
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Information geometry of Markov Kernels: a survey
Frontiers in Physics, 2023 Information geometry and Markov chains are two powerful tools used in modern fields such as finance, physics, computer science, and epidemiology. In this survey, we explore their intersection, focusing on the theoretical framework. We attempt to provide a self-contained treatment of the foundations without requiring a solid background in differential ...
Geoffrey Wolfer, Shun Watanabe
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Rate Functions for Symmetric Markov Processes via Heat Kernel [PDF]
Potential Analysis, 2016 By making full use of heat kernel estimates, we establish the integral tests on the zero-one laws of upper and lower bounds for the sample path ranges of symmetric Markov processes. In particular, these results concerning on upper rate bounds are applicable for local and non-local Dirichlet forms, while lower rate bounds are investigated in both ...
Shiozawa, Yuichi, Wang, Jian
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Monotone bivariate Markov kernels with specified marginals [PDF]
Proceedings of the American Mathematical Society, 2010 Summary: Given two Markov kernels \( k\) and \( k'\) on an ordered Polish space, such that \( k\) is stochastically dominated by \( k'\), we establish the existence of: (i) a monotone bivariate Markov kernel whose marginals are \( k\) and \( k'\) and (ii) an upward coupler from \( k\) to \( k'\). This extends the results of \textit{V.
Machida, Motoya, Shibakov, Alexander
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The Hypergroup Property and Representation of Markov Kernels [PDF]
, 2008 accept\'e au "S\'eminaire de Probabilit\'es"
Bakry, Dominique, Huet, Nolwen
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Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling
SIAM Journal on Mathematics of Data Science, 2022 A novel strategy that combines a given collection of $\pi$-reversible Markov kernels is proposed. At each Markov transition, one of the available kernels is selected via a state-dependent probability distribution. In contrast to random-scan type approaches that assume a constant (i.e.
Maire, Florian, Vandekerkhove, Pierre
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The Category of Markov Kernels
Electronic Notes in Theoretical Computer Science, 1999 AbstractMarkov kernels are fundamental objects in probability theory. One can define a category based on Markov kernels which has many of the formal properties of the ordinary category of relations. In the present paper we will examine the categorical properties of Markov kernels and stress the analogies and differences with the category of relations ...
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Searching for efficient Markov chain Monte Carlo proposal kernels [PDF]
Proceedings of the National Academy of Sciences, 2013 SignificanceBayesian statistics is widely used in various branches of sciences; its main computational method is the Markov chain Monte Carlo (MCMC) algorithm, which is used to simulate a sample on the computer, on which all Bayesian inference is based.
Yang, Z, Rodríguez, CE
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Change Detection of Markov Kernels with Unknown Pre and Post Change Kernel
2022 IEEE 61st Conference on Decision and Control (CDC), 2022 7 pages, 4 ...
Chen, Hao +2 more
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