Results 11 to 20 of about 4,564 (243)
Ergodicity and regime recoverability in finite Markov-modulated random walks [PDF]
Scientific ReportsWe study a finite Markov-modulated random walk (MMRW) on a one-dimensional lattice with reflecting boundaries, where a hidden finite-state Markov environment $$E_t$$ selects at each time step a regime-specific random walk kernel for the position process $
Arina Pambukyan +2 more
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Network motif detection using hidden markov models [PDF]
Scientific ReportsGraphical representations model complex networks by encoding entities as vertices and interactions as edges, with recurring subgraphs—or motifs—revealing fundamental organizational principles. We present a novel application of Hidden Markov Models (HMMs)
Costas Bampos, Vasileios Megalooikonomou
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A note on conditional expectation for Markov kernels [PDF]
Statistics & Probability Letters, 2021 9 pages, 1 ...
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Martin Kernels for Markov Processes with Jumps [PDF]
Potential Analysis, 2017 We prove existence of boundary limits of ratios of positive harmonic functions for a wide class of Markov processes with jumps and irregular domains, in the context of general metric measure spaces. As a corollary, we prove uniqueness of the Martin kernel at each boundary point, that is, we identify the Martin boundary with the topological boundary. We
Kwaśnicki, Mateusz, Juszczyszyn, Tomasz
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Kernel-based Hidden Markov Conditional Densities
SSRN Electronic Journal, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jan G. De Gooijer +2 more
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Approximate Bayesian Computation for Discrete Spaces
Entropy, 2021 Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined.
Ilze A. Auzina, Jakub M. Tomczak
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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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The Hypergroup Property and Representation of Markov Kernels [PDF]
, 2008 For a given orthonormal basis $(f_n)$ on a probability measure space, we want to describe all Markov operators which have the $f_n$ as eigenvectors. We introduce for that what we call the hypergroup property. We study this property in three different cases.
Bakry, Dominique, Huet, Nolwen
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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 ...
Hao Chen +2 more
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