Covariance and relaxation time in finite Markov chains.
Keilson, Julian (1998)
Journal of Applied Mathematics and Stochastic Analysis
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Keilson, Julian (1998)
Journal of Applied Mathematics and Stochastic Analysis
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Jones, Galin L. (2004)
Probability Surveys [electronic only]
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Roberts, Gareth O., Rosenthal, Jeffrey S. (1997)
Electronic Communications in Probability [electronic only]
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R. M. Phatarfod (1983)
Applicationes Mathematicae
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E. Nummelin, R. L. Tweedie (1976)
Annales scientifiques de l'Université de Clermont. Mathématiques
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Ryszard Rębowski (1987)
Colloquium Mathematicae
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Kalashnikov, Vladimir V. (1994)
Journal of Applied Mathematics and Stochastic Analysis
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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...
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.
Häggström, Olle, Rosenthal, Jeffrey S. (2007)
Electronic Communications in Probability [electronic only]
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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,...
M. Courbage, D. Hamdan (2001)
Colloquium Mathematicae
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We construct a large family of ergodic non-Markovian processes with infinite memory having the same p-dimensional marginal laws of an arbitrary ergodic Markov chain or projection of Markov chains. Some of their spectral and mixing properties are given. We show that the Chapman-Kolmogorov equation for the ergodic transition matrix is generically satisfied by infinite memory processes.
Marius Losifescu (1979)
Banach Center Publications
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