Relevant bibliographies by topics / Semi-Markov Models

Academic literature on the topic 'Semi-Markov Models'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Semi-Markov Models"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Semi-Markov Models"

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Bulla, Jan. "Application of Hidden Markov and Hidden Semi-Markov Models to Financial Time Series." Doctoral thesis, [S.l. : s.n.], 2006. http://swbplus.bsz-bw.de/bsz260867136inh.pdf.

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Yeo, Sungchil. "On estimation for a combined Markov and semi-Markov model with censoring /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487586889187169.

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Stenberg, Fredrik. "Semi-Markov models for insurance and option rewards /." Västerås : Department of Mathematics and Physics, Mälardalen University, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-170.

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Choi, K. M. "Semi-Markov and delay time models of maintenance." Thesis, University of Salford, 1997. http://usir.salford.ac.uk/2158/.

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This thesis is concerned with modelling inspection policies of facilities which Qraduallv deteriorate in time. The context of inspection policies lends itself readily to probabilistic modelling. Indeed, many of the published theoretical models to be found in the literature adopt a Markov approach, where states are usually 'operating', 'operating but fault present', and 'failed'. However, most of these models fail to discuss the 'fit' of the model to data,a nd virtually no exampleso f actual applications or case-studiesa re to be found. hi a series of recent papers dating from 1984, a robust approach to solve these problems has been introduced and developed as the Delay Time Model (DTM). The central concept for this model is the delay time, h, of a fault which is the time lapse from when a fault could first be noticed until the time when its repair can be delayed no longer because of unacceptable consequences. The bottle neck in delay time modelling is how to estimate the delay time distribution parameters. Two methods for estimating these parameters have been developed. namely the subjective method and the objective method. Markov models have the advantage of an extensive body of theory. 'fliere are, however. difficulties of definition, measurement, and calculation when applying Markov models to real-world situations within a maintenance context. Indeed. this problem has motivated the current research which ainis to explore the two modelling methodologies in cases where comparison is valid, and also to gain an insight as to how robust Markov inspection models can be as decision-aids where Markovian properties are not strictly satisfied. It Nvill be seen that a class of inspection problems could be solved by a serni- Markov model using the delay time concept. In this thesis, a typical senii-i%Ia, rkov inspection model based upon the delay time concept is presented for a complex repairable systein that may fail during the course of its service lifetime and the results are compared. Finally, a case study of the senii-Markov inspection model and the delay time model is discussed.
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Huang, Xuedong. "Semi-continuous hidden Markov models for speech recognition." Thesis, University of Edinburgh, 1989. http://hdl.handle.net/1842/14125.

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Forsyth, Mark Eric. "Semi-continuous hidden Markov models for automatic speaker verification." Thesis, University of Edinburgh, 1995. http://hdl.handle.net/1842/10903.

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This thesis investigates the use of semi-continuous hidden Markov models (HMM) for automatic speaker verification (ASV) over a telephone channel. The system which was implemented is evaluated on a large database of isolated digits recorded over the British telephone network. The goal of the work is to improve performance of the ASV system under the constraints of limited enrolment data (5 tokens of each digit) and realistic computational and storage requirements. Experiments are conducted on the combined use of several standard feature sets under a common state segmentation, multiple codebook architecture. The feature sets investigated are linear predictive cepstral coefficients, mel-frequency cepstral coefficients and their respective first order differences. New algorithms which are proposed and evaluated include the weighting of digits scores according to their usefulness to the verification task and using Gaussian state duration probabilities as an additional information source in the verification decision. The most important contribution of this thesis is the development of a method for the construction of discriminating HMMs without the need for discriminative training. This new form of model, known as a discriminating observation probability (DOP) HMM involves the combination of standard HMMs to form a discriminating model. The DOP models are more flexible and perform better than the speaker normalisation techniques which are currently favoured in the literature. DOP models have potential application to many binary classification tasks using HMMs. The equal error rate (EER) using speaker specific thresholds on a series of 12 isolated digits was 0.17% using multiple codebook DOP models, compared to 1.93% using single codebook conventional HMM models. This represents a reduction in EER of 91%.
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Bulla, Jan. "Computational Advances and Applications of Hidden (Semi-)Markov Models." Habilitation à diriger des recherches, Université de Caen, 2013. http://tel.archives-ouvertes.fr/tel-00987183.

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The document is my habilitation thesis, which is a prerequisite for obtaining the "habilitation à diriger des recherche (HDR)" in France (https://fr.wikipedia.org/wiki/Habilitation_universitaire#En_France). The thesis is of cumulative form, thus providing an overview of my published works until summer 2013.
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Dingle, Nicholas John. "Parallel computation of response time densities and quantiles in large Markov and semi-Markov models." Thesis, Imperial College London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412007.

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Bondesson, Carl. "Modelling of Safety Concepts for Autonomous Vehicles using Semi-Markov Models." Thesis, Uppsala universitet, Signaler och System, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353060.

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Autonomous vehicles is soon a reality in the every-day life. Though before it is used commercially the vehicles need to be proven safe. The current standard for functional safety on roads, ISO 26262, does not include autonomous vehicles at the moment, which is why in this project an approach using semi-Markov models is used to assess safety. A semi-Markov process is a stochastic process modelled by a state space model where the transitions between the states of the model can be arbitrarily distributed. The approach is realized as a MATLAB tool where the user can use a steady-state based analysis called a Loss and Risk based measure of safety to assess safety. The tool works and can assess safety of semi-Markov systems as long as they are irreducible and positive recurrent. For systems that fulfill these properties, it is possible to draw conclusions about the safety of the system through a risk analysis and also about which autonomous driving level the system is in through a sensitivity analysis. The developed tool, or the approach with the semi-Markov model, might be a good complement to ISO 26262.
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The, Yu-Kai. "Analysis of ion channels with hidden Markov models." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=976048744.

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Books on the topic "Semi-Markov Models"

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Janssen, Jacques, ed. Semi-Markov Models. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1.

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Janssen, Jacques, and Nikolaos Limnios, eds. Semi-Markov Models and Applications. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4613-3288-6.

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1939-, Janssen Jacques, ed. Semi-Markov models: Theory and applications. New York: Plenum Press, 1986.

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D'Amico, Guglielmo, Giuseppe Di Biase, Jacques Janssen, and Raimondo Manca. Semi-Markov Migration Models for Credit Risk. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119415084.

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N, Limnios, ed. Semi-Markov chains and hidden semi-Markov models toward applications: Their use in reliability and DNA analysis. New York: Springer, 2008.

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Salfner, Felix. Modeling event-driven time series with generalized hidden semi-Markov models. Berlin: Humboldt-Universität zu Berlin, 2006.

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Csenki, Attila. Dependability for systems with a partitioned state space: Markov and semi-Markov theory and computational implementation. New York: Springer-Verlag, 1994.

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Jacobs, Patricia A. Estimation of the probability of a long time to the first entrance to a state in a semi-Markov model. Monterey, Calif: Naval Postgraduate School, 1987.

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Semi-Markov Models. Elsevier, 2015. http://dx.doi.org/10.1016/c2014-0-02226-5.

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Hidden Semi-Markov Models. Elsevier, 2016. http://dx.doi.org/10.1016/c2014-0-02508-7.

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Book chapters on the topic "Semi-Markov Models"

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Commenges, Daniel. "Semi-Markov and non-homogeneous Markov models in medicine." In Semi-Markov Models, 423–36. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_25.

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Schäl, Manfred. "Markov and semi-Markov decision models and optimal stopping." In Semi-Markov Models, 39–61. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_4.

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Ney, Peter, and Esa Nummelin. "Some limit theorems for Markov additive processes." In Semi-Markov Models, 3–12. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_1.

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Balcer, Yves, and Izzet Sahin. "Pension accumulation as a semi-Markov reward process, with applications to pension reform." In Semi-Markov Models, 181–99. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_10.

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Schager, Nils Henrik. "The structure of a firm’s optimal non-decreasing wage policy when recruitment is a wage dependent Poisson process." In Semi-Markov Models, 201–13. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_11.

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Korolyuk, V. S. "Markov renewal processes in reliability analysis." In Semi-Markov Models, 217–30. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_12.

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Abdel-Hameed, Mohamed. "Deterioration processes." In Semi-Markov Models, 231–52. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_13.

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Franken, Peter, and Arnfried Streller. "Stochastic processes with an embedded point process and their application to system reliability analysis." In Semi-Markov Models, 253–79. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_14.

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McClean, Sally. "Semi-Markov models for manpower planning." In Semi-Markov Models, 283–300. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_15.

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Keiding, Niels. "Statistical analysis of semi-Markov processes based on the theory of counting processes." In Semi-Markov Models, 301–15. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-0574-1_16.

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Conference papers on the topic "Semi-Markov Models"

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Riedhammer, K., T. Bocklet, A. Ghoshal, and D. Povey. "Revisiting semi-continuous hidden Markov models." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288973.

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Grabski, Franciszek. "Reliability characteristics in semi-Markov models." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4992693.

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Ramesh, A. V., and Kishor Trivedi. "Semi-numerical transient analysis of Markov models." In the 33rd annual. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/1122018.1122021.

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Cotterell, Ryan, Thomas Müller, Alexander Fraser, and Hinrich Schütze. "Labeled Morphological Segmentation with Semi-Markov Models." In Proceedings of the Nineteenth Conference on Computational Natural Language Learning. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/k15-1017.

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Phung, Dinh Q., T. V. Duong, S. Venkatesh, and Hung H. Bui. "Topic transition detection using hierarchical hidden Markov and semi-Markov models." In the 13th annual ACM international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1101149.1101153.

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Lu, Youwei, Shogo Okada, and Katsumi Nitta. "Weibull partition models with applications to hidden semi-Markov models." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7965850.

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Zha, Ya-Bing, Shi-Guang Yue, Quan-Jun Yin, and Xiao-Cheng Liu. "Activity recognition using logical hidden semi-Markov models." In 2013 10th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2013. http://dx.doi.org/10.1109/iccwamtip.2013.6716604.

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Natarajan, Pradeep, and Ramakant Nevatia. "Coupled Hidden Semi Markov Models for Activity Recognition." In 2007 IEEE Workshop on Motion and Video Computing (WMVC'07). IEEE, 2007. http://dx.doi.org/10.1109/wmvc.2007.12.

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Goutsias, John, and Jerry M. Mendel. "Semi-Markov Random Field Models For Texture Synthesis." In Pattern Recognition and Acoustical Imaging, edited by Leonard A. Ferrari. SPIE, 1987. http://dx.doi.org/10.1117/12.940284.

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El-Labban, A., A. Zisserman, Y. Toyoda, A. W. Bird, and A. Hyman. "Discriminative Semi-Markov Models for automated mitotic phase labelling." In 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012). IEEE, 2012. http://dx.doi.org/10.1109/isbi.2012.6235659.

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Reports on the topic "Semi-Markov Models"

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Wereley, Norman M., and Bruce K. Walker. Approximate Evaluation of Semi-Markov Chain Reliability Models,. Fort Belvoir, VA: Defense Technical Information Center, February 1988. http://dx.doi.org/10.21236/ada194669.

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Tollar, Eric S. A Reversal Argument for Storage Models Defined on Semi-Markov Processes. Fort Belvoir, VA: Defense Technical Information Center, February 1986. http://dx.doi.org/10.21236/ada169262.

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