On the Markov chain central limit theorem.
Jones, Galin L. (2004)
Probability Surveys [electronic only]
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Displaying similar documents to “Geometric ergodicity and hybrid Markov chains.”
On the Markov chain central limit theorem.
On infinite dams with Markov dependent inputs
R. M. Phatarfod (1983)
Applicationes Mathematicae
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The Kendall theorem and its application to the geometric ergodicity of Markov chains
Witold Bednorz (2013)
Applicationes Mathematicae
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We give an improved quantitative version of the Kendall theorem. The Kendall theorem states that under mild conditions imposed on a probability distribution on the positive integers (i.e. a probability sequence) one can prove convergence of its renewal sequence. Due to the well-known property (the first entrance last exit decomposition) such results are of interest in the stability theory of time-homogeneous Markov chains. In particular this approach may be used to measure rates of convergence...
On convergence in distribution of the Markov chain generated by the filter kernel induced by a fully dominated Hidden Markov Model
Thomas Kaijser
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Consider a Hidden Markov Model (HMM) such that both the state space and the observation space are complete, separable, metric spaces and for which both the transition probability function (tr.pr.f.) determining the hidden Markov chain of the HMM and the tr.pr.f. determining the observation sequence of the HMM have densities. Such HMMs are called fully dominated. In this paper we consider a subclass of fully dominated HMMs which we call regular. A fully dominated,...
Bibliography on Markov chains with a general state space
Zbyněk Šidák (1976)
Aplikace matematiky
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Covariance and relaxation time in finite Markov chains.
Keilson, Julian (1998)
Journal of Applied Mathematics and Stochastic Analysis
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Geometric ergodicity for a class of Markov chains
E. Nummelin, R. L. Tweedie (1976)
Annales scientifiques de l'Université de Clermont. Mathématiques
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Regeneration and general Markov chains.
Kalashnikov, Vladimir V. (1994)
Journal of Applied Mathematics and Stochastic Analysis
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Why the Kemeny Time is a constant
Karl Gustafson, Jeffrey J. Hunter (2016)
Special Matrices
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We present a new fundamental intuition forwhy the Kemeny feature of a Markov chain is a constant. This new perspective has interesting further implications.
A generalization of Ueno's inequality for n-step transition probabilities
Andrzej Nowak (1998)
Applicationes Mathematicae
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We provide a generalization of Ueno's inequality for n-step transition probabilities of Markov chains in a general state space. Our result is relevant to the study of adaptive control problems and approximation problems in the theory of discrete-time Markov decision processes and stochastic games.