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Journal articles on the topic 'Markov processes'

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Published: 4 June 2021
Last updated: 25 July 2025

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1

Demenkov, N. P., E. A. Mirkin, and I. A. Mochalov. "Markov and Semi-Markov Processes with Fuzzy States. Part 1. Markov Processes." Informacionnye tehnologii 26, no. 6 (2020): 323–34. http://dx.doi.org/10.17587/it.26.323-334.

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2

FRANZ, UWE. "CLASSICAL MARKOV PROCESSES FROM QUANTUM LÉVY PROCESSES." Infinite Dimensional Analysis, Quantum Probability and Related Topics 02, no. 01 (1999): 105–29. http://dx.doi.org/10.1142/s0219025799000060.

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We show how classical Markov processes can be obtained from quantum Lévy processes. It is shown that quantum Lévy processes are quantum Markov processes, and sufficient conditions for restrictions to subalgebras to remain quantum Markov processes are given. A classical Markov process (which has the same time-ordered moments as the quantum process in the vacuum state) exists whenever we can restrict to a commutative subalgebra without losing the quantum Markov property.8 Several examples, including the Azéma martingale, with explicit calculations are presented. In particular, the action of the
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3

Demenkov, N. P., E. A. Mirkin, and I. A. Mochalov. "Markov and Semi-Markov Processes with Fuzzy States. Part 2. Semi-Markov Processes." INFORMACIONNYE TEHNOLOGII 26, no. 7 (2020): 387–93. http://dx.doi.org/10.17587/it.26.387-393.

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4

Whittle, P., and M. L. Puterman. "Markov Decision Processes." Journal of the Royal Statistical Society. Series A (Statistics in Society) 158, no. 3 (1995): 636. http://dx.doi.org/10.2307/2983459.

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5

Smith, J. Q., and D. J. White. "Markov Decision Processes." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, no. 1 (1994): 164. http://dx.doi.org/10.2307/2983520.

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6

King, Aaron A., Qianying Lin, and Edward L. Ionides. "Markov genealogy processes." Theoretical Population Biology 143 (February 2022): 77–91. http://dx.doi.org/10.1016/j.tpb.2021.11.003.

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7

Thomas, L. C., D. J. White, and Martin L. Puterman. "Markov Decision Processes." Journal of the Operational Research Society 46, no. 6 (1995): 792. http://dx.doi.org/10.2307/2584317.

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8

Ephraim, Y., and N. Merhav. "Hidden Markov processes." IEEE Transactions on Information Theory 48, no. 6 (2002): 1518–69. http://dx.doi.org/10.1109/tit.2002.1003838.

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9

Bäuerle, Nicole, and Ulrich Rieder. "Markov Decision Processes." Jahresbericht der Deutschen Mathematiker-Vereinigung 112, no. 4 (2010): 217–43. http://dx.doi.org/10.1365/s13291-010-0007-2.

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10

Wal, J., and J. Wessels. "MARKOV DECISION PROCESSES." Statistica Neerlandica 39, no. 2 (1985): 219–33. http://dx.doi.org/10.1111/j.1467-9574.1985.tb01140.x.

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11

Thomas, L. C. "Markov Decision Processes." Journal of the Operational Research Society 46, no. 6 (1995): 792–93. http://dx.doi.org/10.1057/jors.1995.110.

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12

Frank, T. D. "Nonlinear Markov processes." Physics Letters A 372, no. 25 (2008): 4553–55. http://dx.doi.org/10.1016/j.physleta.2008.04.027.

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13

Kinateder, Kimberly K. J. "Corner Markov processes." Journal of Theoretical Probability 8, no. 3 (1995): 539–47. http://dx.doi.org/10.1007/bf02218043.

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14

Brooks, Stephen, and D. J. White. "Markov Decision Processes." Statistician 44, no. 2 (1995): 292. http://dx.doi.org/10.2307/2348465.

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15

Fagnola, Franco. "Algebraic Markov processes." Proyecciones (Antofagasta) 18, no. 3 (1999): 13–28. http://dx.doi.org/10.22199/s07160917.1999.0003.00003.

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16

Craven, B. D. "Perturbed Markov Processes." Stochastic Models 19, no. 2 (2003): 269–85. http://dx.doi.org/10.1081/stm-120020390.

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17

White, Chelsea C., and Douglas J. White. "Markov decision processes." European Journal of Operational Research 39, no. 1 (1989): 1–16. http://dx.doi.org/10.1016/0377-2217(89)90348-2.

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18

Zhenting, Hou, Liu Zaiming, and Zou Jiezhong. "Markov skeleton processes." Chinese Science Bulletin 43, no. 11 (1998): 881–89. http://dx.doi.org/10.1007/bf02884605.

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19

Franz, Uwe, Volkmar Liebscher, and Stefan Zeiser. "Piecewise-Deterministic Markov Processes as Limits of Markov Jump Processes." Advances in Applied Probability 44, no. 3 (2012): 729–48. http://dx.doi.org/10.1239/aap/1346955262.

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A classical result about Markov jump processes states that a certain class of dynamical systems given by ordinary differential equations are obtained as the limit of a sequence of scaled Markov jump processes. This approach fails if the scaling cannot be carried out equally across all entities. In the present paper we present a convergence theorem for such an unequal scaling. In contrast to an equal scaling the limit process is not purely deterministic but still possesses randomness. We show that these processes constitute a rich subclass of piecewise-deterministic processes. Such processes ap
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20

Franz, Uwe, Volkmar Liebscher, and Stefan Zeiser. "Piecewise-Deterministic Markov Processes as Limits of Markov Jump Processes." Advances in Applied Probability 44, no. 03 (2012): 729–48. http://dx.doi.org/10.1017/s0001867800005851.

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A classical result about Markov jump processes states that a certain class of dynamical systems given by ordinary differential equations are obtained as the limit of a sequence of scaled Markov jump processes. This approach fails if the scaling cannot be carried out equally across all entities. In the present paper we present a convergence theorem for such an unequal scaling. In contrast to an equal scaling the limit process is not purely deterministic but still possesses randomness. We show that these processes constitute a rich subclass of piecewise-deterministic processes. Such processes ap
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21

Buchholz, Peter, and Miklós Telek. "Rational Processes Related to Communicating Markov Processes." Journal of Applied Probability 49, no. 1 (2012): 40–59. http://dx.doi.org/10.1239/jap/1331216833.

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We define a class of stochastic processes, denoted as marked rational arrival processes (MRAPs), which is an extension of matrix exponential distributions and rational arrival processes. Continuous-time Markov processes with labeled transitions are a subclass of this more general model class. New equivalence relations between processes are defined, and it is shown that these equivalence relations are natural extensions of strong and weak lumpability and the corresponding bisimulation relations that have been defined for Markov processes. If a general rational process is equivalent to a Markov
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22

Buchholz, Peter, and Miklós Telek. "Rational Processes Related to Communicating Markov Processes." Journal of Applied Probability 49, no. 01 (2012): 40–59. http://dx.doi.org/10.1017/s0021900200008858.

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We define a class of stochastic processes, denoted as marked rational arrival processes (MRAPs), which is an extension of matrix exponential distributions and rational arrival processes. Continuous-time Markov processes with labeled transitions are a subclass of this more general model class. New equivalence relations between processes are defined, and it is shown that these equivalence relations are natural extensions of strong and weak lumpability and the corresponding bisimulation relations that have been defined for Markov processes. If a general rational process is equivalent to a Markov
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23

Iwata, Yukiko. "Constrictive Markov operators induced by Markov processes." Positivity 20, no. 2 (2015): 355–67. http://dx.doi.org/10.1007/s11117-015-0360-6.

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24

Malinovskii, V. K. "Limit theorems for recurrent semi-Markov processes and Markov renewal processes." Journal of Soviet Mathematics 36, no. 4 (1987): 493–502. http://dx.doi.org/10.1007/bf01663460.

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25

Baddeley, A. J., M. N. M. Van Lieshout, and J. Møller. "Markov properties of cluster processes." Advances in Applied Probability 28, no. 2 (1996): 346–55. http://dx.doi.org/10.2307/1428060.

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We show that a Poisson cluster point process is a nearest-neighbour Markov point process [2] if the clusters have uniformly bounded diameter. It is typically not a finite-range Markov point process in the sense of Ripley and Kelly [12]. Furthermore, when the parent Poisson process is replaced by a Markov or nearest-neighbour Markov point process, the resulting cluster process is also nearest-neighbour Markov, provided all clusters are non-empty. In particular, the nearest-neighbour Markov property is preserved when points of the process are independently randomly translated, but not when they
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26

Baddeley, A. J., M. N. M. Van Lieshout, and J. Møller. "Markov properties of cluster processes." Advances in Applied Probability 28, no. 02 (1996): 346–55. http://dx.doi.org/10.1017/s0001867800048503.

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We show that a Poisson cluster point process is a nearest-neighbour Markov point process [2] if the clusters have uniformly bounded diameter. It is typically not a finite-range Markov point process in the sense of Ripley and Kelly [12]. Furthermore, when the parent Poisson process is replaced by a Markov or nearest-neighbour Markov point process, the resulting cluster process is also nearest-neighbour Markov, provided all clusters are non-empty. In particular, the nearest-neighbour Markov property is preserved when points of the process are independently randomly translated, but not when they
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27

Macheras, Nikolaos D., and Spyridon M. Tzaninis. "Some characterizations for Markov processes as mixed renewal processes." Mathematica Slovaca 68, no. 6 (2018): 1477–94. http://dx.doi.org/10.1515/ms-2017-0196.

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Abstract In this paper the class of mixed renewal processes (MRPs for short) with mixing parameter a random vector defined by Lyberopoulos and Macheras (enlarging Huang’s original class) is replaced by the strictly more comprising class of all extended MRPs by adding a second mixing parameter. We prove under a mild assumption, that within this larger class the basic problem, whether every Markov process is a mixed Poisson process with a random variable as mixing parameter has a solution to the positive. This implies the equivalence of Markov processes, mixed Poisson processes, and processes wi
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28

Fredkin, Donald R., and John A. Rice. "On aggregated Markov processes." Journal of Applied Probability 23, no. 1 (1986): 208–14. http://dx.doi.org/10.2307/3214130.

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29

Kazak, Jolanta. "Piecewise-deterministic Markov processes." Annales Polonici Mathematici 109, no. 3 (2013): 279–96. http://dx.doi.org/10.4064/ap109-3-4.

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30

Lee, P. M., and O. Hernandez-Lerma. "Adaptive Markov Control Processes." Mathematical Gazette 74, no. 470 (1990): 417. http://dx.doi.org/10.2307/3618186.

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31

Kowalski, Zbigniew S. "Multiple Markov Gaussian processes." Applicationes Mathematicae 48, no. 1 (2021): 65–78. http://dx.doi.org/10.4064/am2411-1-2021.

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32

SHIEH, Narn-Rueih. "Collisions of Markov Processes." Tokyo Journal of Mathematics 18, no. 1 (1995): 111–21. http://dx.doi.org/10.3836/tjm/1270043612.

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33

Hawkes, Alan G. "Markov processes in APL." ACM SIGAPL APL Quote Quad 20, no. 4 (1990): 173–85. http://dx.doi.org/10.1145/97811.97843.

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34

Pollett, P. K. "Connecting reversible Markov processes." Advances in Applied Probability 18, no. 4 (1986): 880–900. http://dx.doi.org/10.2307/1427254.

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We provide a framework for interconnecting a collection of reversible Markov processes in such a way that the resulting process has a product-form invariant measure with respect to which the process is reversible. A number of examples are discussed including Kingman&s reversible migration process, interconnected random walks and stratified clustering processes.
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35

Gerontidis, Ioannis I. "Markov population replacement processes." Advances in Applied Probability 27, no. 3 (1995): 711–40. http://dx.doi.org/10.2307/1428131.

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We consider a migration process whose singleton process is a time-dependent Markov replacement process. For the singleton process, which may be treated as either open or closed, we study the limiting distribution, the distribution of the time to replacement and related quantities. For a replacement process in equilibrium we obtain a version of Little's law and we provide conditions for reversibility. For the resulting linear population process we characterize exponential ergodicity for two types of environmental behaviour, i.e. either convergent or cyclic, and finally for large population size
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36

Alexopoulos, Christos, Akram A. El-Tannir, and Richard F. Serfozo. "Partition-Reversible Markov Processes." Operations Research 47, no. 1 (1999): 125–30. http://dx.doi.org/10.1287/opre.47.1.125.

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37

Avrachenkov, Konstantin, Alexey Piunovskiy, and Yi Zhang. "Markov Processes with Restart." Journal of Applied Probability 50, no. 4 (2013): 960–68. http://dx.doi.org/10.1239/jap/1389370093.

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We consider a general homogeneous continuous-time Markov process with restarts. The process is forced to restart from a given distribution at time moments generated by an independent Poisson process. The motivation to study such processes comes from modeling human and animal mobility patterns, restart processes in communication protocols, and from application of restarting random walks in information retrieval. We provide a connection between the transition probability functions of the original Markov process and the modified process with restarts. We give closed-form expressions for the invar
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38

Novak, Stephanie, and Lyman J. Fretwell. "Non‐Markov noise processes." Journal of the Acoustical Society of America 80, S1 (1986): S64. http://dx.doi.org/10.1121/1.2023904.

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39

Rodrigues, Josemar, N. Balakrishnan, and Patrick Borges. "Markov-Correlated Poisson Processes." Communications in Statistics - Theory and Methods 42, no. 20 (2013): 3696–703. http://dx.doi.org/10.1080/03610926.2011.636168.

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40

Larralde, H., F. Leyvraz, and D. P. Sanders. "Metastability in Markov processes." Journal of Statistical Mechanics: Theory and Experiment 2006, no. 08 (2006): P08013. http://dx.doi.org/10.1088/1742-5468/2006/08/p08013.

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41

jun luo, Shou. "Two-parameter markov processes." Stochastics and Stochastic Reports 40, no. 3-4 (1992): 181–93. http://dx.doi.org/10.1080/17442509208833788.

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42

Chin, Y. C., and A. J. Baddeley. "Markov interacting component processes." Advances in Applied Probability 32, no. 3 (2000): 597–619. http://dx.doi.org/10.1239/aap/1013540233.

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A generalization of Markov point processes is introduced in which interactions occur between connected components of the point pattern. A version of the Hammersley-Clifford characterization theorem is proved which states that a point process is a Markov interacting component process if and only if its density function is a product of interaction terms associated with cliques of connected components. Integrability and superpositional properties of the processes are shown and a pairwise interaction example is used for detailed exploration.
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43

Cai, Haiyan. "Piecewise deterministic Markov processes." Stochastic Analysis and Applications 11, no. 3 (1993): 255–74. http://dx.doi.org/10.1080/07362999308809317.

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44

ACCARDI, LUIGI, and ANILESH MOHARI. "TIME REFLECTED MARKOV PROCESSES." Infinite Dimensional Analysis, Quantum Probability and Related Topics 02, no. 03 (1999): 397–425. http://dx.doi.org/10.1142/s0219025799000230.

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A classical stochastic process which is Markovian for its past filtration is also Markovian for its future filtration. We show with a counterexample based on quantum liftings of a finite state classical Markov chain that this property cannot hold in the category of expected Markov processes. Using a duality theory for von Neumann algebras with weights, developed by Petz on the basis of previous results by Groh and Kümmerer, we show that a quantum version of this symmetry can be established in the category of weak Markov processes in the sense of Bhat and Parthasarathy. Here time reversal is im
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45

Fredkin, Donald R., and John A. Rice. "On aggregated Markov processes." Journal of Applied Probability 23, no. 01 (1986): 208–14. http://dx.doi.org/10.1017/s0021900200106412.

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46

Halibard, Moishe, and Ido Kanter. "Markov processes and linguistics." Physica A: Statistical Mechanics and its Applications 249, no. 1-4 (1998): 525–35. http://dx.doi.org/10.1016/s0378-4371(97)00512-8.

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47

Chin, Y. C., and A. J. Baddeley. "Markov interacting component processes." Advances in Applied Probability 32, no. 03 (2000): 597–619. http://dx.doi.org/10.1017/s0001867800010144.

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Abstract:
A generalization of Markov point processes is introduced in which interactions occur between connected components of the point pattern. A version of the Hammersley-Clifford characterization theorem is proved which states that a point process is a Markov interacting component process if and only if its density function is a product of interaction terms associated with cliques of connected components. Integrability and superpositional properties of the processes are shown and a pairwise interaction example is used for detailed exploration.
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48

Pollett, P. K. "Connecting reversible Markov processes." Advances in Applied Probability 18, no. 04 (1986): 880–900. http://dx.doi.org/10.1017/s0001867800016190.

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Abstract:
We provide a framework for interconnecting a collection of reversible Markov processes in such a way that the resulting process has a product-form invariant measure with respect to which the process is reversible. A number of examples are discussed including Kingman&s reversible migration process, interconnected random walks and stratified clustering processes.
APA, Harvard, Vancouver, ISO, and other styles
49

Gerontidis, Ioannis I. "Markov population replacement processes." Advances in Applied Probability 27, no. 03 (1995): 711–40. http://dx.doi.org/10.1017/s0001867800027129.

Full text
Abstract:
We consider a migration process whose singleton process is a time-dependent Markov replacement process. For the singleton process, which may be treated as either open or closed, we study the limiting distribution, the distribution of the time to replacement and related quantities. For a replacement process in equilibrium we obtain a version of Little's law and we provide conditions for reversibility. For the resulting linear population process we characterize exponential ergodicity for two types of environmental behaviour, i.e. either convergent or cyclic, and finally for large population size
APA, Harvard, Vancouver, ISO, and other styles
50

Avrachenkov, Konstantin, Alexey Piunovskiy, and Yi Zhang. "Markov Processes with Restart." Journal of Applied Probability 50, no. 04 (2013): 960–68. http://dx.doi.org/10.1017/s0021900200013735.

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Abstract:
We consider a general homogeneous continuous-time Markov process with restarts. The process is forced to restart from a given distribution at time moments generated by an independent Poisson process. The motivation to study such processes comes from modeling human and animal mobility patterns, restart processes in communication protocols, and from application of restarting random walks in information retrieval. We provide a connection between the transition probability functions of the original Markov process and the modified process with restarts. We give closed-form expressions for the invar
APA, Harvard, Vancouver, ISO, and other styles
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