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Journal articles on the topic 'Semi-Markov Models'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Yu, Shun-Zheng. "Hidden semi-Markov models." Artificial Intelligence 174, no. 2 (February 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, and Roland Langrock. "Semi-Markov Arnason-Schwarz models." Biometrics 72, no. 2 (November 19, 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, no. 9 (September 2011): 4354–66. http://dx.doi.org/10.1016/j.apm.2011.03.006.

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5

D'amico, Guglielmo, Montserrat Guillen, and Raimondo Manca. "Semi-Markov Disability Insurance Models." Communications in Statistics - Theory and Methods 42, no. 16 (August 18, 2013): 2872–88. http://dx.doi.org/10.1080/03610926.2012.746982.

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6

Adams, Stephen, Peter A. Beling, and 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, and Ajay Subramanian. "Heavy tailed hidden semi-markov models." Communications in Statistics. Stochastic Models 14, no. 1-2 (January 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, no. 3 (January 5, 2004): 457–74. http://dx.doi.org/10.1081/sta-120028678.

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9

Verbeken, Brecht, and Marie-Anne Guerry. "Discrete Time Hybrid Semi-Markov Models in Manpower Planning." Mathematics 9, no. 14 (July 16, 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, no. 2 (June 1986): 272–88. http://dx.doi.org/10.1137/1130036.

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11

Janssen, Jacques, Raimondo Manca, and Giuseppe Di Biase. "Markov and semi-Markov option pricing models with arbitrage possibility." Applied Stochastic Models and Data Analysis 13, no. 2 (June 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, and Fidelis Ugwuowo. "Non-homogeneous continuous-time Markov and semi-Markov manpower models." Applied Stochastic Models and Data Analysis 13, no. 3-4 (September 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., and D. Zuckerman. "Semi-Markov shock models with additive damage." Advances in Applied Probability 18, no. 03 (September 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, and 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, no. 4 (1992): 307–15. http://dx.doi.org/10.1093/imaman/4.4.307.

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16

Posner, M. J. M., and D. Zuckerman. "Semi-Markov shock models with additive damage." Advances in Applied Probability 18, no. 3 (September 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, and Onésimo Hernández-Lerma. "Semi-Markov control models with average costs." Applicationes Mathematicae 26, no. 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, and M. Yasin Ulukus. "Semi-Markov models for degradation-based reliability." IIE Transactions 42, no. 8 (May 28, 2010): 599–612. http://dx.doi.org/10.1080/07408170903394371.

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19

Bhattacharya, Rabi N., and Mukul Majumdar. "Controlled semi-markov models - the discounted case." Journal of Statistical Planning and Inference 21, no. 3 (March 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, no. 03 (September 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, no. 3 (September 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, and Simon E. F. Spencer. "Scalable Bayesian Inference for Coupled Hidden Markov and Semi-Markov Models." Journal of Computational and Graphical Statistics 29, no. 2 (September 18, 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, no. 3 (January 5, 2004): 517–31. http://dx.doi.org/10.1081/sta-120028682.

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24

Mitrophanov, Alexander Yu, Alexandre Lomsadze, and Mark Borodovsky. "Sensitivity of hidden Markov models." Journal of Applied Probability 42, no. 03 (September 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, and Mark Borodovsky. "Sensitivity of hidden Markov models." Journal of Applied Probability 42, no. 3 (September 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, and Jaźwiński Jerzy. "Semi-Markov Models of Safety of the Renewal Systems Operation." Journal of Konbin 6, no. 3 (January 1, 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, and Wolfgang Wefelmeyer. "Optimality of estimators for misspecified semi-Markov models." Stochastics 80, no. 2-3 (April 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 (December 2019): 012143. http://dx.doi.org/10.1088/1742-6596/1425/1/012143.

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29

Titman, Andrew C., and Linda D. Sharples. "Semi-Markov Models with Phase-Type Sojourn Distributions." Biometrics 66, no. 3 (November 13, 2009): 742–52. http://dx.doi.org/10.1111/j.1541-0420.2009.01339.x.

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30

Kondrashova, E. V., and V. A. Kashtanov. "Research and optimization of semi-Markov queueing models." Optimization 67, no. 6 (March 7, 2018): 921–41. http://dx.doi.org/10.1080/02331934.2018.1434171.

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31

YIH, YUEHWERN, and ARNE THESEN. "Semi-Markov decision models for real-time scheduling." International Journal of Production Research 29, no. 11 (November 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, no. 3 (May 1999): 272–81. http://dx.doi.org/10.1109/89.759034.

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33

Mukherjee, S. P., and A. K. Chattopadhyay. "Measurement of occupational mobility using semi-markov models." Communications in Statistics - Theory and Methods 18, no. 5 (January 1989): 1961–78. http://dx.doi.org/10.1080/03610928908830013.

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34

Wong, Yung Fei, Lachlan L. H. Andrew, and Y. Ahmet Sekercioglu. "Hidden semi-Markov models for electricity load disaggregation." ACM SIGMETRICS Performance Evaluation Review 46, no. 3 (January 25, 2019): 18–23. http://dx.doi.org/10.1145/3308897.3308906.

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35

Ugwuowo, F. I., and E. F. Udoumoh. "Multi-Stage Semi-Markov Models for Recovery Process." Communications in Statistics - Theory and Methods 40, no. 16 (August 15, 2011): 3011–19. http://dx.doi.org/10.1080/03610926.2011.562787.

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36

Wereley, Norman M., and Bruce K. Walker. "Approximate evaluation of semi-Markov chain reliability models." Reliability Engineering & System Safety 28, no. 2 (January 1990): 133–64. http://dx.doi.org/10.1016/0951-8320(90)90061-q.

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37

Ricciuti, Costantino, and Bruno Toaldo. "Semi-Markov Models and Motion in Heterogeneous Media." Journal of Statistical Physics 169, no. 2 (September 7, 2017): 340–61. http://dx.doi.org/10.1007/s10955-017-1871-2.

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38

Huang, X. D., and M. A. Jack. "Semi-continuous hidden Markov models for speech signals." Computer Speech & Language 3, no. 3 (July 1989): 239–51. http://dx.doi.org/10.1016/0885-2308(89)90020-x.

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39

Barbu, Vlad, and Nikolaos Limnios. "Maximum likelihood estimation for hidden semi-Markov models." Comptes Rendus Mathematique 342, no. 3 (February 2006): 201–5. http://dx.doi.org/10.1016/j.crma.2005.12.013.

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40

Kumar, B. Krishna, and D. Arivudainambi. "Semi-markov compartmental models of invading insect populations." Korean Journal of Computational & Applied Mathematics 7, no. 1 (January 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, no. 2 (August 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, and Mohammad Mohsen Ostadrahimi. "Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models." IPTEK The Journal for Technology and Science 31, no. 3 (January 28, 2021): 295. http://dx.doi.org/10.12962/j20882033.v31i3.7016.

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43

Esquível, Manuel L., Nadezhda P. Krasii, and Gracinda R. Guerreiro. "Open Markov Type Population Models: From Discrete to Continuous Time." Mathematics 9, no. 13 (June 25, 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., and Peter Purdue. "Compartmental models with transfer delays: a semi-Markov approach." Journal of Applied Probability 22, no. 3 (September 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, and D. Blanco-Navarro. "The evaluation problem in discrete semi-hidden Markov models." Mathematics and Computers in Simulation 137 (July 2017): 350–65. http://dx.doi.org/10.1016/j.matcom.2016.12.002.

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46

Sansom, John, and 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, and R. K. Ward. "Offline and online identification of hidden semi-Markov models." IEEE Transactions on Signal Processing 53, no. 8 (August 2005): 2658–63. http://dx.doi.org/10.1109/tsp.2005.850344.

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48

Leitnaker, Mary G., and Peter Purdue. "Compartmental models with transfer delays: a semi-Markov approach." Journal of Applied Probability 22, no. 03 (September 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, and 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, and Andrew Reibman. "Selecting and implementing phase approximations for semi-Markov models." Communications in Statistics. Stochastic Models 9, no. 4 (January 1993): 473–506. http://dx.doi.org/10.1080/15326349308807278.

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