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Journal articles on the topic 'MARKOVIAN APPROACH'

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Published: 11 September 2023

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1

Gilpin, Michael. "Demographic stochasticity: A Markovian approach." Journal of Theoretical Biology 154, no. 1 (January 1992): 1–8. http://dx.doi.org/10.1016/s0022-5193(05)80183-3.

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2

Fahidy, Thomas Z. "A Markovian approach to electrodeposition." Electrochimica Acta 38, no. 8 (June 1993): 1147–48. http://dx.doi.org/10.1016/0013-4686(93)80226-p.

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3

Pollard, Bruce, and Mark Tippett. "Probabilistic Depreciation: A Markovian Approach." British Accounting Review 26, no. 1 (March 1994): 61–76. http://dx.doi.org/10.1006/bare.1994.1006.

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4

Laussel, Didier, and Ngo Van Long. "Vertical Disintegration: A Dynamic Markovian Approach." Journal of Economics & Management Strategy 21, no. 3 (July 4, 2012): 745–71. http://dx.doi.org/10.1111/j.1530-9134.2012.00345.x.

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5

Sen, Kanwar, and J. L. Jain. "Combinatorial approach to Markovian queueing models." Journal of Statistical Planning and Inference 34, no. 2 (February 1993): 269–79. http://dx.doi.org/10.1016/0378-3758(93)90011-t.

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6

Nikolova, M. "Markovian reconstruction using a GNC approach." IEEE Transactions on Image Processing 8, no. 9 (1999): 1204–20. http://dx.doi.org/10.1109/83.784433.

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7

Bertail, P., and S. Clémençon. "A renewal approach to Markovian U-statistics." Mathematical Methods of Statistics 20, no. 2 (June 2011): 79–105. http://dx.doi.org/10.3103/s1066530711020013.

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8

Cheong, C. W. "Web Server Workload Prediction: Fuzzy Markovian Approach." International Journal of Computers and Applications 26, no. 2 (January 2004): 1–6. http://dx.doi.org/10.1080/1206212x.2004.11441730.

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9

Yu, Ting, Lajos Diósi, Nicolas Gisin, and Walter T. Strunz. "Non-Markovian quantum-state diffusion: Perturbation approach." Physical Review A 60, no. 1 (July 1, 1999): 91–103. http://dx.doi.org/10.1103/physreva.60.91.

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10

Cherfi, Abraham, Michel Leeman, Florent Meurville, and Antoine Rauzy. "Modeling automotive safety mechanisms: A Markovian approach." Reliability Engineering & System Safety 130 (October 2014): 42–49. http://dx.doi.org/10.1016/j.ress.2014.04.013.

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11

Guagenti, Elisa, and Chiara Molina. "Structural rehabilitation—A semi-Markovian decision approach." Structural Safety 8, no. 1-4 (July 1990): 255–62. http://dx.doi.org/10.1016/0167-4730(90)90044-p.

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12

Al-Hajjar, Jihad, Olivier Blanpain, and Jean Marc Menu. "Semi-Markovian approach for modelling seismic aftershocks." Engineering Structures 19, no. 12 (December 1997): 969–76. http://dx.doi.org/10.1016/s0141-0296(97)00006-0.

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13

ROMANOS, MICHAEL C., and HASSINE SAIDANE. "A STOCHASTIC MARKOVIAN APPROACH TO TRIP DISTRIBUTION." Papers in Regional Science 41, no. 1 (January 14, 2005): 15–28. http://dx.doi.org/10.1111/j.1435-5597.1978.tb01035.x.

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14

Robb, David John. "Procurement of prescribed sizes: A Markovian approach." European Journal of Operational Research 71, no. 1 (November 1993): 110–19. http://dx.doi.org/10.1016/0377-2217(93)90264-n.

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15

Putz, Mihai V. "Markovian approach of the electron localization functions." International Journal of Quantum Chemistry 105, no. 1 (2005): 1–11. http://dx.doi.org/10.1002/qua.20645.

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16

BASU, B., V. K. GUPTA, and D. KUNDU. "A MARKOVIAN APPROACH TO ORDERED PEAK STATISTICS." Earthquake Engineering & Structural Dynamics 25, no. 12 (December 1996): 1335–51. http://dx.doi.org/10.1002/(sici)1096-9845(199612)25:12<1335::aid-eqe596>3.0.co;2-o.

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17

Corn, Brittany, Jun Jing, and Ting Yu. "Non-Markovian quantum trajectroy unravellings of entanglement." Quantum Information and Computation 16, no. 5&6 (April 2016): 483–97. http://dx.doi.org/10.26421/qic16.5-6-5.

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The fully quantized model of double qubits coupled to a common bath is solved using the quantum state diffusion (QSD) approach in the non-Markovian regime. We have established the explicit time-local non-Markovian QSD equations for the two-qubit dissipative and dephasing models. Diffusive quantum trajectories are applied to the entanglement estimation of two-qubit systems in a non-Markovian regime. In both cases, non-Markovian features of entanglement evolution are revealed through quantum diffusive unravellings in the system state space.
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18

Semina, I., V. Semin, F. Petruccione, and A. Barchielli. "Stochastic Schrödinger Equations for Markovian and non-Markovian Cases." Open Systems & Information Dynamics 21, no. 01n02 (March 12, 2014): 1440008. http://dx.doi.org/10.1142/s1230161214400083.

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Firstly, the Markovian stochastic Schrödinger equations are presented, together with their connections with the theory of measurements in continuous time. Moreover, the stochastic evolution equations are translated into a simulation algorithm, which is illustrated by two concrete examples — the damped harmonic oscillator and a two-level atom with homodyne photodetection. We then consider how to introduce memory effects in the stochastic Schrödinger equation via coloured noise. Specifically, the approach by using the Ornstein-Uhlenbeck process is illustrated and a simulation for the non-Markovian process proposed. Finally, an analytical approximation technique is tested with the help of the stochastic simulation in a model of a dissipative qubit.
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19

Xue, Shibei, Rebing Wu, Tzyh-Jong Tarn, and Ian R. Petersen. "Witnessing the boundary between Markovian and non-Markovian quantum dynamics: a Green’s function approach." Quantum Information Processing 14, no. 7 (April 24, 2015): 2657–72. http://dx.doi.org/10.1007/s11128-015-1000-6.

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20

Musso, Marcello, and Ravi K. Sheth. "On the Markovian assumption in the excursion set approach: the approximation of Markovian Velocities." Monthly Notices of the Royal Astronomical Society 443, no. 2 (July 23, 2014): 1601–13. http://dx.doi.org/10.1093/mnras/stu1222.

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21

Zheng, Cheng-De. "Stochastic stability of fuzzy Markovian jump neural networks by multiple integral approach." International Journal of Intelligent Computing and Cybernetics 11, no. 1 (March 12, 2018): 81–105. http://dx.doi.org/10.1108/ijicc-11-2016-0046.

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Purpose The purpose of this paper is to develop a methodology for the stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square. Design/methodology/approach The authors perform Briat Lemma, multiple integral approach and linear convex combination technique to investigate a class of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay. New sufficient criterion is established by linear matrix inequalities conditions. Findings It turns out that the obtained methods are easy to be verified and result in less conservative conditions than the existing literature. Two examples show the effectiveness of the proposed results. Originality/value The novelty of the proposed approach lies in establishing a new Wirtinger-based integral inequality and the use of the Lyapunov functional method, Briat Lemma, multiple integral approach and linear convex combination technique for stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square.
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22

Tarasov, Vasily E. "General Non-Markovian Quantum Dynamics." Entropy 23, no. 8 (July 31, 2021): 1006. http://dx.doi.org/10.3390/e23081006.

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A general approach to the construction of non-Markovian quantum theory is proposed. Non-Markovian equations for quantum observables and states are suggested by using general fractional calculus. In the proposed approach, the non-locality in time is represented by operator kernels of the Sonin type. A wide class of the exactly solvable models of non-Markovian quantum dynamics is suggested. These models describe open (non-Hamiltonian) quantum systems with general form of nonlocality in time. To describe these systems, the Lindblad equations for quantum observable and states are generalized by taking into account a general form of nonlocality. The non-Markovian quantum dynamics is described by using integro-differential equations with general fractional derivatives and integrals with respect to time. The exact solutions of these equations are derived by using the operational calculus that is proposed by Yu. Luchko for general fractional differential equations. Properties of bi-positivity, complete positivity, dissipativity, and generalized dissipativity in general non-Markovian quantum dynamics are discussed. Examples of a quantum oscillator and two-level quantum system with a general form of nonlocality in time are suggested.
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23

Das, Ashok K., Sudhakar Panda, and J. R. L. Santos. "A path integral approach to the Langevin equation." International Journal of Modern Physics A 30, no. 07 (March 5, 2015): 1550028. http://dx.doi.org/10.1142/s0217751x15500281.

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We study the Langevin equation with both a white noise and a colored noise. We construct the Lagrangian as well as the Hamiltonian for the generalized Langevin equation which leads naturally to a path integral description from first principles. This derivation clarifies the meaning of the additional fields introduced by Martin, Siggia and Rose in their functional formalism. We show that the transition amplitude, in this case, is the generating functional for correlation functions. We work out explicitly the correlation functions for the Markovian process of the Brownian motion of a free particle as well as for that of the non-Markovian process of the Brownian motion of a harmonic oscillator (Uhlenbeck–Ornstein model). The path integral description also leads to a simple derivation of the Fokker–Planck equation for the generalized Langevin equation.
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24

Chen, Yusui, J. Q. You, and Ting Yu. "Non-Markovian quantum interference in multilevel quantum systems: exact master equation approach." Quantum Information and Computation 18, no. 15&16 (December 2018): 1261–71. http://dx.doi.org/10.26421/qic18.15-16-1.

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We study the non-Markovian dynamics of multilevel quantum systems coupled with a bosonic dissipative environment. Based on the known exact quantum-state diffusion (QSD) equations, we propose a systematic approach to derive exact time-convolutionless master equations for multilevel quantum systems. Through a combination of analytical and numerical approaches, we extract the non-Markovian dynamics of quantum interference in different time scales. Also, we demonstrate the evolution of quantum interference in a four-level system controlled by an external electromagnetic field. Our findings are extended to few-body quantum networks, with a universal formalism established.
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25

Cedillo-Campos, Miguel Gastón, José Luis de la Riva-Canizales, Alfredo Bueno-Solano, Jesús Gonzalez-Feliu, and Jorge Luis García-Alcaraz. "Reliability in urban freight distribution: A Markovian approach." DYNA 81, no. 187 (October 24, 2014): 232–39. http://dx.doi.org/10.15446/dyna.v81n187.46105.

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26

Basari, Abd Samad Hasan, Hazlina Razali, Burairah Hussin, Siti Azirah Asmai, Nuzulha Khilwani Ibrahim, and Abdul Samad Shibghatullah. "Markovian approach enhancement to simplify optimal mean estimation." Applied Mathematical Sciences 8 (2014): 5507–16. http://dx.doi.org/10.12988/ams.2014.45373.

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27

Silva, Marcelino S. da, Carlos R. L. Francês, João C. W. A. Costa, Diego L. Cardoso, Nandamudi L. Vijaykumar, and Solon V. de Carvalho. "QoS management in smart grids: a Markovian approach." Journal of Microwaves, Optoelectronics and Electromagnetic Applications 13, no. 2 (December 2014): 122–38. http://dx.doi.org/10.1590/s2179-10742014000200002.

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28

Chiang, S. Y., C. T. Kuo, and S. M. Meerkov. "Bottlenecks in Markovian production lines: a systems approach." IEEE Transactions on Robotics and Automation 14, no. 2 (April 1998): 352–59. http://dx.doi.org/10.1109/70.681256.

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29

Imamog¯lu, A. "Stochastic wave-function approach to non-Markovian systems." Physical Review A 50, no. 5 (November 1, 1994): 3650–53. http://dx.doi.org/10.1103/physreva.50.3650.

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30

Bellomo, Bruno, Antonella De Pasquale, Giulia Gualdi, and Ugo Marzolino. "A tomographic approach to non-Markovian master equations." Journal of Physics A: Mathematical and Theoretical 43, no. 39 (August 26, 2010): 395303. http://dx.doi.org/10.1088/1751-8113/43/39/395303.

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31

Piunovskiy, A. B., and X. Mao. "Constrained Markovian decision processes: the dynamic programming approach." Operations Research Letters 27, no. 3 (October 2000): 119–26. http://dx.doi.org/10.1016/s0167-6377(00)00039-0.

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32

Buchholz, Peter, and Miklós Telek. "Rational Automata Networks: A Non-Markovian Modeling Approach." INFORMS Journal on Computing 25, no. 1 (February 2013): 87–101. http://dx.doi.org/10.1287/ijoc.1110.0487.

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33

Bonitz, M., J. W. Dufty, and Cheng Sub Kim. "BBGKY Approach to Non-Markovian Semiconductor Bloch Equations." physica status solidi (b) 206, no. 1 (March 1998): 181–87. http://dx.doi.org/10.1002/(sici)1521-3951(199803)206:1<181::aid-pssb181>3.0.co;2-0.

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34

Topper, Noah, George Atia, Ashutosh Trivedi, and Alvaro Velasquez. "Active Grammatical Inference for Non-Markovian Planning." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 647–51. http://dx.doi.org/10.1609/icaps.v32i1.19853.

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Planning in finite stochastic environments is canonically posed as a Markov decision process where the transition and reward structures are explicitly known. Reinforcement learning (RL) lifts the explicitness assumption by working with sampling models instead. Further, with the advent of reward machines, we can relax the Markovian assumption on the reward. Angluin's active grammatical inference algorithm L* has found novel application in explicating reward machines for non-Markovian RL. We propose maintaining the assumption of explicit transition dynamics, but with an implicit non-Markovian reward signal, which must be inferred from experiments. We call this setting non-Markovian planning, as opposed to non-Markovian RL. The proposed approach leverages L* to explicate an automaton structure for the underlying planning objective. We exploit the environment model to learn an automaton faster and integrate it with value iteration to accelerate the planning. We compare against recent non-Markovian RL solutions which leverage grammatical inference, and establish complexity results that illustrate the difference in runtime between grammatical inference in planning and RL settings.
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35

Akbari, Nasim, Ali Sadr, and Ali Kazemy. "Robust exponential synchronization of a Markovian jump complex dynamical network with piecewise homogeneous Markovian parameters." IMA Journal of Mathematical Control and Information 37, no. 4 (April 7, 2020): 1168–91. http://dx.doi.org/10.1093/imamci/dnz041.

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Abstract This paper establishes a stochastic synchronization method for a Markovian jump complex dynamical network (MJCDN) with time-delay and uncertainties. The considered Markovian structure is piecewise-homogeneous with piecewise-constant time-varying transition rates (TRs). Two Markovian signals are utilized to construct the piecewise-homogeneous Markovian structure. A low-level Markovian signal with time-varying TRs governs the switching between the system dynamics while it is managed by a high-level Markovian signal. Due to the effect of imperfections induced by modeling errors in the system dynamics, some parametric norm-bounded uncertainties are considered. In addition, uncertain TR matrix is considered which means that inaccurate or uncertain information for each element of the TR matrix is allowable. This modelling makes the MJCDN to be more general and applicable than the existing ones. Synchronization conditions are obtained and reported in the form of linear matrix inequalities by the help of Lyapunov–Krasovskii theory, Wirtinger-based integral inequality approach and reciprocally convex technique. Finally, a numerical example is presented to verify the effectiveness of the proposed method.
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36

Izvekov, Yury, Oleg Tulupov, Vladislav Dubrovsky, and Alexey Kanel-Belov. "Stochastic Processes Occurring during the Transition of Technical State of the Structure." MATEC Web of Conferences 346 (2021): 03036. http://dx.doi.org/10.1051/matecconf/202134603036.

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The possibility of applying the theory of stochastic processes for evaluating the dynamic pattern of different states is studied for critical-duty structures. Heterogeneous Markovian processes of technical state transition for metallurgical overhead crane structure, Markovian theorem and Kolmogorov-Chapman equation are analyzed. Markovian chain is reviewed at t →∞, i.e. under marginal steady-state (stabilized) condition. Real values of limit probabilities are obtained for the structure of the metallurgical overhead crane under review. The proposed approach redefines and elaborates the existing methods and procedures for evaluating the technical state of structures and reduces the level of ambiguity associated with such kind of problems.
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37

Zhou, Wuneng, Anding Dai, Dongbing Tong, and Jun Yang. "Exponential Synchronization of Stochastic Complex Dynamical Networks with Impulsive Perturbations and Markovian Switching." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/927858.

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This paper investigates the exponential synchronization problem of stochastic complex dynamical networks with impulsive perturbation and Markovian switching. The complex dynamical networks consist ofκmodes, and the networks switch from one mode to another according to a Markovian chain with known transition probability. Based on the Lyapunov function method and stochastic analysis, by employingM-matrix approach, some sufficient conditions are presented to ensure the exponential synchronization of stochastic complex dynamical networks with impulsive perturbation and Markovian switching, and the upper bound of impulsive gain is evaluated. At the end of this paper, two numerical examples are included to show the effectiveness of our results.
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38

Špička, Václav, Bedřich Velický, and Anděla Kalvová. "Electron systems out of equilibrium: Nonequilibrium Green's function approach." International Journal of Modern Physics B 28, no. 23 (July 13, 2014): 1430013. http://dx.doi.org/10.1142/s0217979214300138.

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This review deals with the state of the art and perspectives of description of nonequilibrium many-body systems using the nonequilibrium Green's function (NGF) method. The basic aim is to describe time evolution of the many-body system from its initial state over its transient dynamics to its long time asymptotic evolution. First, we discuss basic aims of transport theories to motivate the introduction of the NGF techniques. Second, this article summarizes the present view on construction of the electron transport equations formulated within the NGF approach to nonequilibrium. We discuss incorporation of complex initial conditions to the NGF formalism, and the NGF reconstruction theorem, which serves as a tool to derive simplified kinetic equations. Three stages of evolution of the nonequilibrium, the first described by the full NGF description, the second by a non-Markovian generalized master equation and the third by a Markovian master equation will be related to each other.
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39

Cui, Lirong, Alan Hawkes, and He Yi. "An elementary derivation of moments of Hawkes processes." Advances in Applied Probability 52, no. 1 (March 2020): 102–37. http://dx.doi.org/10.1017/apr.2019.53.

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AbstractHawkes processes have been widely used in many areas, but their probability properties can be quite difficult. In this paper an elementary approach is presented to obtain moments of Hawkes processes and/or the intensity of a number of marked Hawkes processes, in which the detailed outline is given step by step; it works not only for all Markovian Hawkes processes but also for some non-Markovian Hawkes processes. The approach is simpler and more convenient than usual methods such as the Dynkin formula and martingale methods. The method is applied to one-dimensional Hawkes processes and other related processes such as Cox processes, dynamic contagion processes, inhomogeneous Poisson processes, and non-Markovian cases. Several results are obtained which may be useful in studying Hawkes processes and other counting processes. Our proposed method is an extension of the Dynkin formula, which is simple and easy to use.
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40

Nie, Yanyi, Wenyao Li, Liming Pan, Tao Lin, and Wei Wang. "Markovian approach to tackle competing pathogens in simplicial complex." Applied Mathematics and Computation 417 (March 2022): 126773. http://dx.doi.org/10.1016/j.amc.2021.126773.

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41

Filip, Dariusz, and Tomasz Rogala. "Analysis of Polish mutual funds performance: a Markovian approach." Statistics in Transition New Series 22, no. 1 (2021): 115–30. http://dx.doi.org/10.21307/stattrans-2021-006.

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42

Lucente, D., A. Petri, and A. Vulpiani. "A Markovian approach to the Prandtl–Tomlinson frictional model." Physica A: Statistical Mechanics and its Applications 572 (June 2021): 125899. http://dx.doi.org/10.1016/j.physa.2021.125899.

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43

Aberkane, S. "Reconfigurable Control Systems: A Nonhomogeneous Markovian Jump System Approach." IFAC Proceedings Volumes 44, no. 1 (January 2011): 5425–29. http://dx.doi.org/10.3182/20110828-6-it-1002.02376.

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44

Li, Jingshan, and Ningjian Huang. "Quality Evaluation in Flexible Manufacturing Systems: A Markovian Approach." Mathematical Problems in Engineering 2007 (2007): 1–24. http://dx.doi.org/10.1155/2007/57128.

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The flexible manufacturing system (FMS) has attracted substantial amount of research effort during the last twenty years. Most of the studies address the issues of flexibility, productivity, cost, and so forth. The impact of flexible lines on product quality is less studied. This paper intends to address this issue by applying a Markov model to evaluate quality performance of a flexible manufacturing system. Closed expressions to calculate good part probability are derived and discussions to maintain high product quality are carried out. An example of flexible fixture in machining system is provided to illustrate the applicability of the method. The results of this study suggest a possible approach to investigate the impact of flexibility on product quality and, finally, with extensions and enrichment of the model, may lead to provide production engineers and managers a better understanding of the quality implications and to summarize some general guidelines of operation management in flexible manufacturing systems.
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45

Carruba, V., F. Roig, T. A. Michtchenko, S. Ferraz-Mello, and D. Nesvorný. "Modeling close encounters with massive asteroids: a Markovian approach." Astronomy & Astrophysics 465, no. 1 (January 11, 2007): 315–30. http://dx.doi.org/10.1051/0004-6361:20066056.

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46

Fain, Benjamin. "Relaxation via spontaneous emission of bosons: Non-Markovian approach." Physical Review A 37, no. 2 (January 1, 1988): 546–58. http://dx.doi.org/10.1103/physreva.37.546.

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47

Luh, Peter B., Yaowen Yu, Bingjie Zhang, Eugene Litvinov, Tongxin Zheng, Feng Zhao, Jinye Zhao, and Congcong Wang. "Grid Integration of Intermittent Wind Generation: A Markovian Approach." IEEE Transactions on Smart Grid 5, no. 2 (March 2014): 732–41. http://dx.doi.org/10.1109/tsg.2013.2268462.

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48

Melodelima, Christelle, Christian Gautier, and Didier Piau. "A markovian approach for the prediction of mouse isochores." Journal of Mathematical Biology 55, no. 3 (May 8, 2007): 353–64. http://dx.doi.org/10.1007/s00285-007-0087-5.

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49

Bottazzi, F., Th M. Liebling, J. L. Scartezzini, and M. Nygård-Ferguson. "On a Markovian approach for modeling passive solar devices." Energy and Buildings 17, no. 2 (January 1991): 103–16. http://dx.doi.org/10.1016/0378-7788(91)90003-l.

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50

Lee, Byung Kwon, Loo Hay Lee, and Ek Peng Chew. "Analysis on container port capacity: a Markovian modeling approach." OR Spectrum 36, no. 2 (January 17, 2013): 425–54. http://dx.doi.org/10.1007/s00291-012-0318-z.

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