Results 11 to 20 of about 4,387 (252)
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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Transportation inequalities for Markov kernels and their applications [PDF]
Electronic Journal of Probability, 2021 38 pages.
Baudoin, Fabrice, Eldredge, Nathaniel
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Martin Kernels for Markov Processes with Jumps [PDF]
Potential Analysis, 2017 20 ...
Kwaśnicki, Mateusz, Juszczyszyn, Tomasz
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Turning Simulation into Estimation: Generalized Exchange Algorithms for Exponential Family Models. [PDF]
PLoS ONE, 2017 The Single Variable Exchange algorithm is based on a simple idea; any model that can be simulated can be estimated by producing draws from the posterior distribution.
Maarten Marsman +3 more
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Fuzzy Observables: from Weak Markov Kernels to Markov Kernels
International Journal of Theoretical Physics, 2023 AbstractWe provide a proof based on transfinite induction that every weak Markov kernel is equivalent to a Markov kernel. We only assume the space where the weak Markov kernel is defined to be second countable and metrizable. That generalizes some previous results where the kernel is required to be defined on a standard Borel space (which is second ...
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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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Polynomial ergodicity of Markov transition kernels
Stochastic Processes and their Applications, 2003 Let \(\varPhi \) be a time-homogeneous Markov chain on a countably generated measure space \((X,\mathfrak B)\) with a \(\varphi \)-irreducible and aperiodic transition kernel \(P\). Let \(f\geq 1\) be a measurable function on \(X\) and \(r=(r(n))_ {n\geq 1}\) a polynomial sequence, that is, \(\liminf r(n)(n+1)^ {-\beta }>0\), \(\limsup r(n)(n+1 ...
Fort, G., Moulines, E.
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Heavy Tailed Approximate Identities and σ-stable Markov Kernels [PDF]
Potential Analysis, 2017 The aim of this paper is to present some results relating the properties of stability, concentration and approximation to the identity of convolution through not necessarily mollification type families of heavy tailed Markov kernels. A particular case is provided by the kernels $K_t$ obtained as the $t$ mollification of $L^{ (t)}$ selected from the ...
Aimar, Hugo Alejandro +2 more
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A note on conditional expectation for Markov kernels [PDF]
Statistics & Probability Letters, 2021 9 pages, 1 ...
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Derivatives of Markov Kernels and Their Jordan Decomposition [PDF]
Journal of Applied Analysis, 2008 Let \((P_\vartheta)_{\vartheta \in \Theta}\) be a parametric family of Markov kernels from a measurable space \((X, \mathcal{X})\) to a locally compact space \(Y\). The family \((P_\vartheta)_{\vartheta \in \Theta}\) is called weakly differentiable at \(\vartheta\) if for any \(x \in X\) there is a finite signed Baire measure \(P'_\vartheta(x, .)\) on \
Heidergott, B.F. +2 more
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