Steady-state distributions of piecewise Markov processes
Maria Jankiewicz, B. Kopociński (1976)
Applicationes Mathematicae
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Maria Jankiewicz, B. Kopociński (1976)
Applicationes Mathematicae
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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,...
Jeffrey J. Hunter (2016)
Special Matrices
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This article describes an accurate procedure for computing the mean first passage times of a finite irreducible Markov chain and a Markov renewal process. The method is a refinement to the Kohlas, Zeit fur Oper Res, 30, 197–207, (1986) procedure. The technique is numerically stable in that it doesn’t involve subtractions. Algebraic expressions for the special cases of one, two, three and four states are derived.Aconsequence of the procedure is that the stationary distribution of the...
Zbyněk Šidák (1976)
Aplikace matematiky
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Laurent Mazliak (2007)
Revue d'histoire des mathématiques
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We present the letters sent by Wolfgang Doeblin to Bohuslav Hostinský between 1936 and 1938. They concern some aspects of the general theory of Markov chains and the solutions of the Chapman-Kolmogorov equation that Doeblin was then establishing for his PhD thesis.
Maria Jankiewicz (1978)
Applicationes Mathematicae
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Tomasz R. Bielecki, Jacek Jakubowski, Mariusz Niewęgłowski (2015)
Banach Center Publications
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In this paper we study finite state conditional Markov chains (CMCs). We give two examples of CMCs, one which admits intensity, and another one, which does not admit an intensity. We also give a sufficient condition under which a doubly stochastic Markov chain is a CMC. In addition we provide a method for construction of conditional Markov chains via change of measure.
W. P. Cherry, R. L. Disney (1983)
Applicationes Mathematicae
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G. Kallianpur, V. Mandrekar (1974)
Annales de l'institut Fourier
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We obtain necessary and sufficient conditions in order that a Gaussian process of many parameters (more generally, a generalized Gaussian random field in ) possess the Markov property relative to a class of open sets. The method adopted is the Hilbert space approach initiated by Cartier and Pitt. Applications are discussed.
Franco Giannessi (2002)
RAIRO - Operations Research - Recherche Opérationnelle
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A problem (arisen from applications to networks) is posed about the principal minors of the matrix of transition probabilities of a Markov chain.