Results 251 to 260 of about 5,432,397 (302)
Identification and quantification of irreversibility in stochastic systems.
Ghosal A, Bisker G.
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Theory of Probability & Its Applications, 1956
Let $\mathcal{E}$ be a metric space, and suppose that $\mathfrak{B}$ is the Borel field generated by the open sets of $\mathcal{E}$. A stochastic process is defined on $\mathcal{E}$ if a function $x(t,\omega )$$(0 \leqq t < \infty ,\omega \in \Omega )$ and a system of probability measures ${\bf P}_x (x \in \mathcal{E})$ are given such that all ${\bf P ...
Dynkin, E. B., Yushkevich, A. A.
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Let $\mathcal{E}$ be a metric space, and suppose that $\mathfrak{B}$ is the Borel field generated by the open sets of $\mathcal{E}$. A stochastic process is defined on $\mathcal{E}$ if a function $x(t,\omega )$$(0 \leqq t < \infty ,\omega \in \Omega )$ and a system of probability measures ${\bf P}_x (x \in \mathcal{E})$ are given such that all ${\bf P ...
Dynkin, E. B., Yushkevich, A. A.
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IEEE Transactions on Information Theory, 2002
Summary: An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of \textit{L. E. Baum} and \textit{R.E. Petrie}, Ann. Math. Stat.
Yariv Ephraim, Neri Merhav
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Summary: An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of \textit{L. E. Baum} and \textit{R.E. Petrie}, Ann. Math. Stat.
Yariv Ephraim, Neri Merhav
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Statistica Neerlandica, 1985
AbstractA review is presented of the development over the years of the theory and practical use of Markov decision processes. To this purpose three periods are considered: before 1966, from 1966 till 1972, and after 1973. In all 3 periods there has been some contribution from the Netherlands, but particularly in the last period the research in the ...
Wal, van der, J., Wessels, J.
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AbstractA review is presented of the development over the years of the theory and practical use of Markov decision processes. To this purpose three periods are considered: before 1966, from 1966 till 1972, and after 1973. In all 3 periods there has been some contribution from the Netherlands, but particularly in the last period the research in the ...
Wal, van der, J., Wessels, J.
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Markov Functionals of an Ergodic Markov Process
Theory of Probability & Its Applications, 1995Let \((X(t), t \geq 0)\) be a homogeneous Markov process. The author calls a random process \(\xi(t)\) the Markovian functional if the pair \((X(t), \xi(t))\) is a homogeneous Markov process. Let \(\xi_n (t)\) be a sequence of Markovian functionals with finite state space \(I = \{1,2,\dots, d\}\) for all \(n \geq 1\) and such that the following ...
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Representation of a Semi-Markov Process as a Time-Change Markov Process
Theory of Probability & Its Applications, 1984Translation from Teor. Veroyatn. Primen. 28, No.4, 653-667 (Russian) (1983; Zbl 0539.60088).
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Nature, 1970
Markov processes are encountered in many contexts in physics and chemistry. This review surveys the scope of their application.
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Markov processes are encountered in many contexts in physics and chemistry. This review surveys the scope of their application.
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SIAM Journal on Applied Mathematics, 1973
A piecewise Markov process is a discrete-state, continuous-parameter stochastic process which is Markovian within contiguous time-segments. Starting at the beginning of a segment in some initial state, the process evolves in a Markovian manner until the segment terminates at a random time whose distribution is completely determined by the initial state.
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A piecewise Markov process is a discrete-state, continuous-parameter stochastic process which is Markovian within contiguous time-segments. Starting at the beginning of a segment in some initial state, the process evolves in a Markovian manner until the segment terminates at a random time whose distribution is completely determined by the initial state.
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Symmetrizations of Markov processes
Journal of Theoretical Probability, 1988The authors discuss two methods of symmetrizing Markov processes. The first method applies to some Lévy processes X on a compact Abelian group G, with \(\alpha\)-potential density \(u^{\alpha}(x,y)\). A condition is given which guarantees that \(v^{\alpha}(x,y)=u^{\alpha}(x,y)+u^{\alpha}(y,x)\) will be the \(\alpha\)-potential density of a symmetric ...
Glover, Joseph, Rao, Murali
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