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Artículos de revistas sobre el tema "Semi-Markov Models"

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Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 1 de febrero de 2022

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1

Yu, Shun-Zheng. "Hidden semi-Markov models". Artificial Intelligence 174, n.º 2 (febrero de 2010): 215–43. http://dx.doi.org/10.1016/j.artint.2009.11.011.

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2

Obzherin, Yuriy E. "Semi-Markov and hidden semi-Markov models of energy systems". E3S Web of Conferences 58 (2018): 02023. http://dx.doi.org/10.1051/e3sconf/20185802023.

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The problem of information control systems creation for energy systems and transition to intelligent control and engineering is one of the important problems of reliability and efficiency theory for energy systems. The solution of this problem is possible based on construction of mathematical models concerning different aspects of these systems structure and operation. The possibilities of application of semi-Markov processes with common phase space of states, hidden Markov and semi-Markov models for energy system modeling are considered in the paper.
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3

King, Ruth y Roland Langrock. "Semi-Markov Arnason-Schwarz models". Biometrics 72, n.º 2 (19 de noviembre de 2015): 619–28. http://dx.doi.org/10.1111/biom.12446.

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4

D’Amico, Guglielmo. "Age-usage semi-Markov models". Applied Mathematical Modelling 35, n.º 9 (septiembre de 2011): 4354–66. http://dx.doi.org/10.1016/j.apm.2011.03.006.

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5

D'amico, Guglielmo, Montserrat Guillen y Raimondo Manca. "Semi-Markov Disability Insurance Models". Communications in Statistics - Theory and Methods 42, n.º 16 (18 de agosto de 2013): 2872–88. http://dx.doi.org/10.1080/03610926.2012.746982.

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6

Adams, Stephen, Peter A. Beling y Randy Cogill. "Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models". IEEE Access 4 (2016): 1642–57. http://dx.doi.org/10.1109/access.2016.2552478.

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7

Resnick, Sidney y Ajay Subramanian. "Heavy tailed hidden semi-markov models". Communications in Statistics. Stochastic Models 14, n.º 1-2 (enero de 1998): 319–34. http://dx.doi.org/10.1080/15326349808807474.

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8

Huzurbazar, Aparna V. "Multistate Models, Flowgraph Models, and Semi-Markov Processes". Communications in Statistics - Theory and Methods 33, n.º 3 (5 de enero de 2004): 457–74. http://dx.doi.org/10.1081/sta-120028678.

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9

Verbeken, Brecht y Marie-Anne Guerry. "Discrete Time Hybrid Semi-Markov Models in Manpower Planning". Mathematics 9, n.º 14 (16 de julio de 2021): 1681. http://dx.doi.org/10.3390/math9141681.

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Discrete time Markov models are used in a wide variety of social sciences. However, these models possess the memoryless property, which makes them less suitable for certain applications. Semi-Markov models allow for more flexible sojourn time distributions, which can accommodate for duration of stay effects. An overview of differences and possible obstacles regarding the use of Markov and semi-Markov models in manpower planning was first given by Valliant and Milkovich (1977). We further elaborate on their insights and introduce hybrid semi-Markov models for open systems with transition-dependent sojourn time distributions. Hybrid semi-Markov models aim to reduce model complexity in terms of the number of parameters to be estimated by only taking into account duration of stay effects for those transitions for which it is useful. Prediction equations for the stock vector are derived and discussed. Furthermore, the insights are illustrated and discussed based on a real world personnel dataset. The hybrid semi-Markov model is compared with the Markov and the semi-Markov models by diverse model selection criteria.
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10

Kitayev, M. Yu. "Semi-Markov and Jump Markov Controlled Models: Average Cost Criterion". Theory of Probability & Its Applications 30, n.º 2 (junio de 1986): 272–88. http://dx.doi.org/10.1137/1130036.

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11

Janssen, Jacques, Raimondo Manca y Giuseppe Di Biase. "Markov and semi-Markov option pricing models with arbitrage possibility". Applied Stochastic Models and Data Analysis 13, n.º 2 (junio de 1997): 103–13. http://dx.doi.org/10.1002/(sici)1099-0747(199706)13:2<103::aid-asm299>3.0.co;2-z.

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12

McClean, Sally, Erin Montgomery y Fidelis Ugwuowo. "Non-homogeneous continuous-time Markov and semi-Markov manpower models". Applied Stochastic Models and Data Analysis 13, n.º 3-4 (septiembre de 1997): 191–98. http://dx.doi.org/10.1002/(sici)1099-0747(199709/12)13:3/4<191::aid-asm312>3.0.co;2-t.

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13

Posner, M. J. M. y D. Zuckerman. "Semi-Markov shock models with additive damage". Advances in Applied Probability 18, n.º 03 (septiembre de 1986): 772–90. http://dx.doi.org/10.1017/s0001867800016062.

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We examine a replacement model for a semi-Markov shock model with additive damage. Sufficient conditions are given for the optimality of control limit policies. The paper generalizes and unifies previous research in the area. In addition, we investigate in detail the practical modelling and computational aspects of the replacement problem using a semi-Markov modelling structure.
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14

Cartella, Francesco, Jan Lemeire, Luca Dimiccoli y Hichem Sahli. "Hidden Semi-Markov Models for Predictive Maintenance". Mathematical Problems in Engineering 2015 (2015): 1–23. http://dx.doi.org/10.1155/2015/278120.

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Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.
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15

MCCLEAN, SALLY. "Semi-Markov models for human-resource modelling". IMA Journal of Management Mathematics 4, n.º 4 (1992): 307–15. http://dx.doi.org/10.1093/imaman/4.4.307.

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16

Posner, M. J. M. y D. Zuckerman. "Semi-Markov shock models with additive damage". Advances in Applied Probability 18, n.º 3 (septiembre de 1986): 772–90. http://dx.doi.org/10.2307/1427187.

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We examine a replacement model for a semi-Markov shock model with additive damage. Sufficient conditions are given for the optimality of control limit policies. The paper generalizes and unifies previous research in the area.In addition, we investigate in detail the practical modelling and computational aspects of the replacement problem using a semi-Markov modelling structure.
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17

Luque-Vásquez, Fernando y Onésimo Hernández-Lerma. "Semi-Markov control models with average costs". Applicationes Mathematicae 26, n.º 3 (1999): 315–31. http://dx.doi.org/10.4064/am-26-3-315-331.

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18

Kharoufeh, Jeffrey P., Christopher J. Solo y M. Yasin Ulukus. "Semi-Markov models for degradation-based reliability". IIE Transactions 42, n.º 8 (28 de mayo de 2010): 599–612. http://dx.doi.org/10.1080/07408170903394371.

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19

Bhattacharya, Rabi N. y Mukul Majumdar. "Controlled semi-markov models - the discounted case". Journal of Statistical Planning and Inference 21, n.º 3 (marzo de 1989): 365–81. http://dx.doi.org/10.1016/0378-3758(89)90053-0.

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20

Gerontidis, Ioannis I. "Semi-Markov Replacement Chains". Advances in Applied Probability 26, n.º 03 (septiembre de 1994): 728–55. http://dx.doi.org/10.1017/s0001867800026525.

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We consider an absorbing semi-Markov chain for which each time absorption occurs there is a resetting of the chain according to some initial (replacement) distribution. The new process is a semi-Markov replacement chain and we study its properties in terms of those of the imbedded Markov replacement chain. A time-dependent version of the model is also defined and analysed asymptotically for two types of environmental behaviour, i.e. either convergent or cyclic. The results contribute to the control theory of semi-Markov chains and extend in a natural manner a wide variety of applied probability models. An application to the modelling of populations with semi-Markovian replacements is also presented.
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21

Gerontidis, Ioannis I. "Semi-Markov Replacement Chains". Advances in Applied Probability 26, n.º 3 (septiembre de 1994): 728–55. http://dx.doi.org/10.2307/1427818.

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We consider an absorbing semi-Markov chain for which each time absorption occurs there is a resetting of the chain according to some initial (replacement) distribution. The new process is a semi-Markov replacement chain and we study its properties in terms of those of the imbedded Markov replacement chain. A time-dependent version of the model is also defined and analysed asymptotically for two types of environmental behaviour, i.e. either convergent or cyclic. The results contribute to the control theory of semi-Markov chains and extend in a natural manner a wide variety of applied probability models. An application to the modelling of populations with semi-Markovian replacements is also presented.
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22

Touloupou, Panayiota, Bärbel Finkenstädt y Simon E. F. Spencer. "Scalable Bayesian Inference for Coupled Hidden Markov and Semi-Markov Models". Journal of Computational and Graphical Statistics 29, n.º 2 (18 de septiembre de 2019): 238–49. http://dx.doi.org/10.1080/10618600.2019.1654880.

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23

Anisimov, Vladimir V. "Averaging in Markov Models with Fast Semi-Markov Switches and Applications". Communications in Statistics - Theory and Methods 33, n.º 3 (5 de enero de 2004): 517–31. http://dx.doi.org/10.1081/sta-120028682.

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24

Mitrophanov, Alexander Yu, Alexandre Lomsadze y Mark Borodovsky. "Sensitivity of hidden Markov models". Journal of Applied Probability 42, n.º 03 (septiembre de 2005): 632–42. http://dx.doi.org/10.1017/s002190020000067x.

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We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix and the initial distribution of the underlying Markov chain. Our approach can also be used to assess the sensitivity of other stochastic models, such as mixture processes and semi-Markov processes.
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25

Mitrophanov, Alexander Yu, Alexandre Lomsadze y Mark Borodovsky. "Sensitivity of hidden Markov models". Journal of Applied Probability 42, n.º 3 (septiembre de 2005): 632–42. http://dx.doi.org/10.1239/jap/1127322017.

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We derive a tight perturbation bound for hidden Markov models. Using this bound, we show that, in many cases, the distribution of a hidden Markov model is considerably more sensitive to perturbations in the emission probabilities than to perturbations in the transition probability matrix and the initial distribution of the underlying Markov chain. Our approach can also be used to assess the sensitivity of other stochastic models, such as mixture processes and semi-Markov processes.
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26

Franciszek, Grabski y Jaźwiński Jerzy. "Semi-Markov Models of Safety of the Renewal Systems Operation". Journal of Konbin 6, n.º 3 (1 de enero de 2008): 153–76. http://dx.doi.org/10.2478/v10040-008-0064-0.

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Semi-Markov Models of Safety of the Renewal Systems Operation Usually, the renewal systems are mathematically described by the alternating renewal process, which is special case of the semi-Markov process. It allows us to find the basic reliability characteristics like the renewal function, the operational availability of the system and many other. Many papers are devoted to that problem. Very often the operation of the system is perturbed by the danger events. The operation process of the system is broken or stopped. In that case we can say that the unsafety event occurred. Semi-Markov model of the safety of operation is constructed in this paper. The safety characteristics of the system operation are calculated by using the properties of the semi-Markov processes.
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27

Müller, Ursula U., Anton Schick y Wolfgang Wefelmeyer. "Optimality of estimators for misspecified semi-Markov models". Stochastics 80, n.º 2-3 (abril de 2008): 181–96. http://dx.doi.org/10.1080/17442500701841008.

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28

Khayrullin, R. Z. "About applicability of semi-Markov models of operation". Journal of Physics: Conference Series 1425 (diciembre de 2019): 012143. http://dx.doi.org/10.1088/1742-6596/1425/1/012143.

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29

Titman, Andrew C. y Linda D. Sharples. "Semi-Markov Models with Phase-Type Sojourn Distributions". Biometrics 66, n.º 3 (13 de noviembre de 2009): 742–52. http://dx.doi.org/10.1111/j.1541-0420.2009.01339.x.

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30

Kondrashova, E. V. y V. A. Kashtanov. "Research and optimization of semi-Markov queueing models". Optimization 67, n.º 6 (7 de marzo de 2018): 921–41. http://dx.doi.org/10.1080/02331934.2018.1434171.

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31

YIH, YUEHWERN y ARNE THESEN. "Semi-Markov decision models for real-time scheduling". International Journal of Production Research 29, n.º 11 (noviembre de 1991): 2331–46. http://dx.doi.org/10.1080/00207549108948086.

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32

Gales, M. J. F. "Semi-tied covariance matrices for hidden Markov models". IEEE Transactions on Speech and Audio Processing 7, n.º 3 (mayo de 1999): 272–81. http://dx.doi.org/10.1109/89.759034.

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33

Mukherjee, S. P. y A. K. Chattopadhyay. "Measurement of occupational mobility using semi-markov models". Communications in Statistics - Theory and Methods 18, n.º 5 (enero de 1989): 1961–78. http://dx.doi.org/10.1080/03610928908830013.

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34

Wong, Yung Fei, Lachlan L. H. Andrew y Y. Ahmet Sekercioglu. "Hidden semi-Markov models for electricity load disaggregation". ACM SIGMETRICS Performance Evaluation Review 46, n.º 3 (25 de enero de 2019): 18–23. http://dx.doi.org/10.1145/3308897.3308906.

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35

Ugwuowo, F. I. y E. F. Udoumoh. "Multi-Stage Semi-Markov Models for Recovery Process". Communications in Statistics - Theory and Methods 40, n.º 16 (15 de agosto de 2011): 3011–19. http://dx.doi.org/10.1080/03610926.2011.562787.

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36

Wereley, Norman M. y Bruce K. Walker. "Approximate evaluation of semi-Markov chain reliability models". Reliability Engineering & System Safety 28, n.º 2 (enero de 1990): 133–64. http://dx.doi.org/10.1016/0951-8320(90)90061-q.

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37

Ricciuti, Costantino y Bruno Toaldo. "Semi-Markov Models and Motion in Heterogeneous Media". Journal of Statistical Physics 169, n.º 2 (7 de septiembre de 2017): 340–61. http://dx.doi.org/10.1007/s10955-017-1871-2.

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38

Huang, X. D. y M. A. Jack. "Semi-continuous hidden Markov models for speech signals". Computer Speech & Language 3, n.º 3 (julio de 1989): 239–51. http://dx.doi.org/10.1016/0885-2308(89)90020-x.

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39

Barbu, Vlad y Nikolaos Limnios. "Maximum likelihood estimation for hidden semi-Markov models". Comptes Rendus Mathematique 342, n.º 3 (febrero de 2006): 201–5. http://dx.doi.org/10.1016/j.crma.2005.12.013.

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40

Kumar, B. Krishna y D. Arivudainambi. "Semi-markov compartmental models of invading insect populations". Korean Journal of Computational & Applied Mathematics 7, n.º 1 (enero de 2000): 161–74. http://dx.doi.org/10.1007/bf03009934.

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41

Crowder, Martin. "Semi-Markov Chains and Hidden Semi-Markov Models Toward Applications by Vlad Stefan Barbu, Nikolaos Limnios". International Statistical Review 77, n.º 2 (agosto de 2009): 307. http://dx.doi.org/10.1111/j.1751-5823.2009.00085_8.x.

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42

Kordnoori, Shirin, Hamidreza Mostafaei, Shaghayegh Kordnoori y Mohammad Mohsen Ostadrahimi. "Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models". IPTEK The Journal for Technology and Science 31, n.º 3 (28 de enero de 2021): 295. http://dx.doi.org/10.12962/j20882033.v31i3.7016.

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43

Esquível, Manuel L., Nadezhda P. Krasii y Gracinda R. Guerreiro. "Open Markov Type Population Models: From Discrete to Continuous Time". Mathematics 9, n.º 13 (25 de junio de 2021): 1496. http://dx.doi.org/10.3390/math9131496.

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We address the problem of finding a natural continuous time Markov type process—in open populations—that best captures the information provided by an open Markov chain in discrete time which is usually the sole possible observation from data. Given the open discrete time Markov chain, we single out two main approaches: In the first one, we consider a calibration procedure of a continuous time Markov process using a transition matrix of a discrete time Markov chain and we show that, when the discrete time transition matrix is embeddable in a continuous time one, the calibration problem has optimal solutions. In the second approach, we consider semi-Markov processes—and open Markov schemes—and we propose a direct extension from the discrete time theory to the continuous time one by using a known structure representation result for semi-Markov processes that decomposes the process as a sum of terms given by the products of the random variables of a discrete time Markov chain by time functions built from an adequate increasing sequence of stopping times.
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44

Leitnaker, Mary G. y Peter Purdue. "Compartmental models with transfer delays: a semi-Markov approach". Journal of Applied Probability 22, n.º 3 (septiembre de 1985): 570–82. http://dx.doi.org/10.2307/3213861.

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Compartmental models for which transfer from one compartment to another takes a non-negligible time have been studied in the deterministic case. These models rely on the use of differential equations with delayed arguments. In this paper we show how the well-known structure of the semi-Markov process can be used to analyse stochastic compartmental models with transfer delays. Evaluation of the limiting behavior is much simpler in the stochastic model than in previous deterministic formulations. In addition, time-dependent behavior can be analysed using numerical quadrature methods.
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45

Gómez-Lopera, J. F., J. Martínez-Aroza, R. Román-Roldán, R. Román-Gálvez y D. Blanco-Navarro. "The evaluation problem in discrete semi-hidden Markov models". Mathematics and Computers in Simulation 137 (julio de 2017): 350–65. http://dx.doi.org/10.1016/j.matcom.2016.12.002.

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46

Sansom, John y Peter Thomson. "Fitting hidden semi-Markov models to breakpoint rainfall data". Journal of Applied Probability 38, A (2001): 142–57. http://dx.doi.org/10.1017/s0021900200112744.

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The paper proposes a hidden semi-Markov model for breakpoint rainfall data that consist of both the times at which rain-rate changes and the steady rates between such changes. The model builds on and extends the seminal work of Ferguson (1980) on variable duration models for speech. For the rainfall data the observations are modelled as mixtures of log-normal distributions within unobserved states where the states evolve in time according to a semi-Markov process. For the latter, parametric forms need to be specified for the state transition probabilities and dwell-time distributions. Recursions for constructing the likelihood are developed and the EM algorithm used to fit the parameters of the model. The choice of dwell-time distribution is discussed with a mixture of distributions over disjoint domains providing a flexible alternative. The methods are also extended to deal with censored data. An application of the model to a large-scale bivariate dataset of breakpoint rainfall measurements at Wellington, New Zealand, is discussed.
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47

Azimi, M., P. Nasiopoulos y R. K. Ward. "Offline and online identification of hidden semi-Markov models". IEEE Transactions on Signal Processing 53, n.º 8 (agosto de 2005): 2658–63. http://dx.doi.org/10.1109/tsp.2005.850344.

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48

Leitnaker, Mary G. y Peter Purdue. "Compartmental models with transfer delays: a semi-Markov approach". Journal of Applied Probability 22, n.º 03 (septiembre de 1985): 570–82. http://dx.doi.org/10.1017/s0021900200029338.

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Compartmental models for which transfer from one compartment to another takes a non-negligible time have been studied in the deterministic case. These models rely on the use of differential equations with delayed arguments. In this paper we show how the well-known structure of the semi-Markov process can be used to analyse stochastic compartmental models with transfer delays. Evaluation of the limiting behavior is much simpler in the stochastic model than in previous deterministic formulations. In addition, time-dependent behavior can be analysed using numerical quadrature methods.
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49

Barbu, Vlad Stefan, Guglielmo D’Amico, Raimondo Manca y Filippo Petroni. "Step semi-Markov models and application to manpower management". ESAIM: Probability and Statistics 20 (2016): 555–71. http://dx.doi.org/10.1051/ps/2016025.

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50

Malhotra, Manish y Andrew Reibman. "Selecting and implementing phase approximations for semi-Markov models". Communications in Statistics. Stochastic Models 9, n.º 4 (enero de 1993): 473–506. http://dx.doi.org/10.1080/15326349308807278.

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