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Relevant bibliographies by topics / Markov chains non-homogeneous

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Academic literature on the topic 'Markov chains non-homogeneous'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "Markov chains non-homogeneous"

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Dey, Agnish, and Arunava Mukherjea. "Collapsing of non-homogeneous Markov chains." Statistics & Probability Letters 84 (January 2014): 140–48. http://dx.doi.org/10.1016/j.spl.2013.10.002.

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Pawłowski, Janusz. "Poisson theorem for non-homogeneous Markov chains." Journal of Applied Probability 26, no. 3 (September 1989): 637–42. http://dx.doi.org/10.2307/3214421.

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Řezníček, Jan, Martin Kohlík, and Hana Kubátová. "Non-homogeneous hierarchical Continuous Time Markov Chains." Microprocessors and Microsystems 78 (October 2020): 103206. http://dx.doi.org/10.1016/j.micpro.2020.103206.

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Pawłowski, Janusz. "Poisson theorem for non-homogeneous Markov chains." Journal of Applied Probability 26, no. 03 (September 1989): 637–42. http://dx.doi.org/10.1017/s0021900200038237.

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Tang, Ying, Weiguo Yang, and Yue Zhang. "THE STRONG LIMIT THEOREM FOR RELATIVE ENTROPY DENSITY RATES BETWEEN TWO ASYMPTOTICALLY CIRCULAR MARKOV CHAINS." Probability in the Engineering and Informational Sciences 33, no. 2 (April 2, 2018): 161–71. http://dx.doi.org/10.1017/s0269964818000074.

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In this paper, we are going to study the strong limit theorem for the relative entropy density rates between two finite asymptotically circular Markov chains. Firstly, we prove some lammas on which the main result based. Then, we establish two strong limit theorem for non-homogeneous Markov chains. Finally, we obtain the main result of this paper. As corollaries, we get the strong limit theorem for the relative entropy density rates between two finite non-homogeneous Markov chains. We also prove that the relative entropy density rates between two finite non-homogeneous Markov chains are uniformly integrable under some conditions.

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Hadjiloucas, Demetris. "Stochastic matrix-valued cocycles and non-homogeneous Markov chains." Discrete & Continuous Dynamical Systems - A 17, no. 4 (2007): 731–38. http://dx.doi.org/10.3934/dcds.2007.17.731.

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Zeifman, A. I. "Quasi-ergodicity for non-homogeneous continuous-time Markov chains." Journal of Applied Probability 26, no. 3 (September 1989): 643–48. http://dx.doi.org/10.2307/3214422.

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We consider a non-homogeneous continuous-time Markov chain X(t) with countable state space. Definitions of uniform and strong quasi-ergodicity are introduced. The forward Kolmogorov system for X(t) is considered as a differential equation in the space of sequences l1. Sufficient conditions for uniform quasi-ergodicity are deduced from this equation. We consider conditions of uniform and strong ergodicity in the case of proportional intensities.

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Zeifman, A. I. "Quasi-ergodicity for non-homogeneous continuous-time Markov chains." Journal of Applied Probability 26, no. 03 (September 1989): 643–48. http://dx.doi.org/10.1017/s0021900200038249.

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We consider a non-homogeneous continuous-time Markov chain X(t) with countable state space. Definitions of uniform and strong quasi-ergodicity are introduced. The forward Kolmogorov system for X(t) is considered as a differential equation in the space of sequences l 1 . Sufficient conditions for uniform quasi-ergodicity are deduced from this equation. We consider conditions of uniform and strong ergodicity in the case of proportional intensities.

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Bisbas, Antonis. "A Cassels–Schmidt theorem for non-homogeneous Markov chains." Bulletin des Sciences Mathématiques 129, no. 1 (January 2005): 25–37. http://dx.doi.org/10.1016/j.bulsci.2004.06.002.

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Vassiliou, P. C. G. "On the periodicity of non-homogeneous Markov chains and systems." Linear Algebra and its Applications 471 (April 2015): 654–84. http://dx.doi.org/10.1016/j.laa.2015.01.017.

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Dissertations / Theses on the topic "Markov chains non-homogeneous"

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Hubbard, Rebecca Allana. "Modeling a non-homogeneous Markov process via time transformation /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/9607.

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Nascimento, Ant?nio Marcos Batista do. "Ergodicidade em cadeias de Markov n?o-homog?neas e cadeias de Markov com transi??es raras." Universidade Federal do Rio Grande do Norte, 2014. http://repositorio.ufrn.br:8080/jspui/handle/123456789/18651.

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Made available in DSpace on 2015-03-03T15:32:44Z (GMT). No. of bitstreams: 1 AntonioMBN_DISSERT.pdf: 717546 bytes, checksum: 381030bb759313d7ef41203fde24db9f (MD5) Previous issue date: 2014-02-14
The central objective of a study Non-Homogeneous Markov Chains is the concept of weak and strong ergodicity. A chain is weak ergodic if the dependence on the initial distribution vanishes with time, and it is strong ergodic if it is weak ergodic and converges in distribution. Most theoretical results on strong ergodicity assume some knowledge of the limit behavior of the stationary distributions. In this work, we collect some general results on weak and strong ergodicity for chains with space enumerable states, and also study the asymptotic behavior of the stationary distributions of a particular type of Markov Chains with finite state space, called Markov Chains with Rare Transitions
O objetivo central de estudo em Cadeias de Markov N?o-Homog?neas e o conceito de ergodicidade fraca e forte. Uma cadeia ? erg?dica fraca se a depend?ncia da distribui??o inicial desaparece com o tempo, e ? erg?dica forte se ? erg?dica fraca e converge em distribui??o. A maioria dos resultados te?ricos sobre a ergodicidade forte sup?e algum conhecimento do comportamento limite das distribui??es estacion?rias. Neste trabalho, reunimos alguns resultados gerais sobre ergodicidade fraca e forte para cadeias com espa?oo de estados enumer?vel, e tamb?m estudamos o comportamento assint?tico das distribui??es estacion?rias de um tipo particular de Cadeias de Markov com espa?o de estados nito, chamadas Cadeias de Markov com Transi??es Raras

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Sandels, Claes. "Modeling and Simulation of Electricity Consumption Profiles in the Northern European Building Stock." Doctoral thesis, KTH, Elkraftteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-184093.

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The electric power systems are currently being transformed through the integration of intermittent renewable energy resources and new types of electric loads. These developments run the risk of increasing mismatches between electricity supply and demand, and may cause non-favorable utilization rates of some power system components. Using Demand Response (DR) from flexible loads in the building stock is a promising solution to overcome these challenges for electricity market actors. However, as DR is not used at a large scale today, there are validity concerns regarding its cost-benefit and reliability when compared to traditional investment options in the power sector, e.g. network refurbishment. To analyze the potential in DR solutions, bottom-up simulation models which capture consumption processes in buildings is an alternative. These models must be simple enough to allow aggregations of buildings to be instantiated and at the same time intricate enough to include variations in individual behaviors of end-users. This is done so the electricity market actor can analyze how large volumes of flexibility acts in various market and power system operation contexts, but also can appreciate how individual end-users are affected by DR actions in terms of cost and comfort. The contribution of this thesis is bottom-up simulation models for generating load profiles in detached houses and office buildings. The models connect end-user behavior with the usage of appliances and hot water loads through non-homogenous Markov chains, along with physical modeling of the indoor environment and consumption of heating and cooling loads through lumped capacitance models. The modeling is based on a simplified approach where openly available data and statistics are used, i.e. data that is subject to privacy limitations, such as smart meter measurements are excluded. The models have been validated using real load data from detached houses and office buildings, related models in literature, along with energy-use statistics from national databases. The validation shows that the modeling approach is sound and can provide reasonably accurate load profiles as the error results are in alignment with related models from other research groups. This thesis is a composite thesis of five papers. Paper 1 presents a bottom-up simulation model to generate load profiles from space heating, hot water and appliances in detached houses. Paper 2 presents a data analytic framework for analyzing electricity-use from heating ventilation and air conditioning (HVAC) loads and appliance loads in an office building. Paper 3 presents a non-homogeneous Markov chain model to simulate representative occupancy profiles in single office rooms. Paper 4 utilizes the results in paper 2 and 3 to describe a bottom-up simulation model that generates load profiles in office buildings including HVAC loads and appliances. Paper 5 uses the model in paper 1 to analyze the technical feasibility of using DR to solve congestion problems in a distribution grid.
Integrering av förnybara energikällor och nya typer av laster i de elektriska energisystemen är möjliga svar till klimatförändringar och uttömning av ändliga naturresurser. Denna integration kan dock öka obalanserna mellan utbud och efterfrågan av elektricitet, och orsaka en ogynnsam utnyttjandegrad av vissa kraftsystemkomponenter. Att använda efterfrågeflexibilitet (Demand Response) i byggnadsbeståndet är en möjlig lösning till dessa problem för olika elmarknadsaktörer. Men eftersom efterfrågeflexibilitet inte används i stor skala idag finns det obesvarade frågor gällande lösningens kostnadsnytta och tillförlitlighet jämfört med traditionella investeringsalternativ i kraftsektorn. För att analysera efterfrågeflexibilitetslösningar är botten-upp-simuleringsmodeller som fångar elförbrukningsprocesser i byggnaderna ett alternativ. Dessa modeller måste vara enkla nog för att kunna representera aggregeringar av många byggnader men samtidigt tillräckligt komplicerade för att kunna inkludera unika slutanvändarbeteenden. Detta är nödvändigt när elmarknadsaktören vill analysera hur stora volymer efterfrågeflexibilitet påverkar elmarknaden och kraftsystemen, men samtidigt förstå hur styrningen inverkar på den enskilda slutanvändaren. Bidraget från denna avhandling är botten-upp-simuleringsmodeller för generering av elförbrukningsprofiler i småhus och kontorsbyggnader. Modellerna kopplar slutanvändarbeteende med elförbrukning från apparater och varmvattenanvändning tillsammans med fysikaliska modeller av värmedynamiken i byggnaderna. Modellerna är byggda på en förenklad approach som använder öppen data och statistisk, där data som har integritetsproblem har exkluderats. Simuleringsresultat har validerats mot elförbrukningsdata från småhus och kontorsbyggnader, relaterade modeller från andra forskargrupper samt energistatistik från nationella databaser. Valideringen visar att modellerna kan generera elförbrukningsprofiler med rimlig noggrannhet. Denna avhandling är en sammanläggningsavhandling bestående av fem artiklar. Artikel 1 presenterar botten-upp-simuleringsmodellen för genereringen av elförbrukningsprofiler från uppvärmning, varmvatten och apparater i småhus. Artikel 2 presenterar ett dataanalytiskt ramverk för analys av elanvändningen från uppvärmning, ventilation, och luftkonditioneringslaster (HVAC) och apparatlaster i en kontorsbyggnad. Artikel 3 presenterar en icke-homogen Markovkedjemodell för simulering av representativa närvaroprofiler i enskilda kontorsrum. Artikel 4 använder resultaten i artiklarna 2 och 3 för att beskriva en botten-upp-simuleringsmodell för generering av elförbrukningsprofiler från HVAC-laster och apparater i kontorsbyggnader. Artikel 5 använder modellen i artikel 1 för att analysera den tekniska möjligheten att använda efterfrågeflexibilitet för att lösa överbelastningsproblem i ett eldistributionsnät.

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Davis, Brett Andrew, and Brett Davis@abs gov au. "Inference for Discrete Time Stochastic Processes using Aggregated Survey Data." The Australian National University. Faculty of Economics and Commerce, 2003. http://thesis.anu.edu.au./public/adt-ANU20040806.104137.

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We consider a longitudinal system in which transitions between the states are governed by a discrete time finite state space stochastic process X. Our aim, using aggregated sample survey data of the form typically collected by official statistical agencies, is to undertake model based inference for the underlying process X. We will develop inferential techniques for continuing sample surveys of two distinct types. First, longitudinal surveys in which the same individuals are sampled in each cycle of the survey. Second, cross-sectional surveys which sample the same population in successive cycles but with no attempt to track particular individuals from one cycle to the next. Some of the basic results have appeared in Davis et al (2001) and Davis et al (2002).¶ Longitudinal surveys provide data in the form of transition frequencies between the states of X. In Chapter Two we develop a method for modelling and estimating the one-step transition probabilities in the case where X is a non-homogeneous Markov chain and transition frequencies are observed at unit time intervals. However, due to their expense, longitudinal surveys are typically conducted at widely, and sometimes irregularly, spaced time points. That is, the observable frequencies pertain to multi-step transitions. Continuing to assume the Markov property for X, in Chapter Three, we show that these multi-step transition frequencies can be stochastically interpolated to provide accurate estimates of the one-step transition probabilities of the underlying process. These estimates for a unit time increment can be used to calculate estimates of expected future occupation time, conditional on an individuals state at initial point of observation, in the different states of X.¶ For reasons of cost, most statistical collections run by official agencies are cross-sectional sample surveys. The data observed from an on-going survey of this type are marginal frequencies in the states of X at a sequence of time points. In Chapter Four we develop a model based technique for estimating the marginal probabilities of X using data of this form. Note that, in contrast to the longitudinal case, the Markov assumption does not simplify inference based on marginal frequencies. The marginal probability estimates enable estimation of future occupation times (in each of the states of X) for an individual of unspecified initial state. However, in the applications of the technique that we discuss (see Sections 4.4 and 4.5) the estimated occupation times will be conditional on both gender and initial age of individuals.¶ The longitudinal data envisaged in Chapter Two is that obtained from the surveillance of the same sample in each cycle of an on-going survey. In practice, to preserve data quality it is necessary to control respondent burden using sample rotation. This is usually achieved using a mechanism known as rotation group sampling. In Chapter Five we consider the particular form of rotation group sampling used by the Australian Bureau of Statistics in their Monthly Labour Force Survey (from which official estimates of labour force participation rates are produced). We show that our approach to estimating the one-step transition probabilities of X from transition frequencies observed at incremental time intervals, developed in Chapter Two, can be modified to deal with data collected under this sample rotation scheme. Furthermore, we show that valid inference is possible even when the Markov property does not hold for the underlying process.

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Feng, Siwei. "Wavelet-Based Non-Homogeneous Hidden Markov Chain Model For Hyperspectral Signature Classification." 2015. https://scholarworks.umass.edu/masters_theses_2/145.

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Hyperspectral signature classification is a kind of quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from corresponding hyperspectral signatures containing information like signature energy or shape. In this paper, we describe a technique that applies non-homogeneous hidden Markov chain (NHMC) models to hyperspectral signature classification. The basic idea is to use statistical models (NHMC models) to characterize wavelet coefficients which capture the spectrum structural information at multiple levels. Experimental results show that the approach based on NHMC models outperforms existing approaches relevant in classification tasks.

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Book chapters on the topic "Markov chains non-homogeneous"

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Brémaud, Pierre. "Non-homogeneous Markov Chains." In Texts in Applied Mathematics, 399–422. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45982-6_12.

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Platis, Agapios, Nikolaos Limnios, and Marc Le Du. "Electrical Substation Performability and Reliability Indicators Modelled by Non-Homogeneous Markov Chains." In Probabilistic Safety Assessment and Management ’96, 709–14. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-3409-1_113.

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Jin, Shaohua, Yanmin Zhang, Yanping Wan, Nan Li, and Huipeng Zhang. "A Strong Deviation Theorem for Markov Chains Functional Indexed by a Non-homogeneous Tree." In Advanced Technology in Teaching - Proceedings of the 2009 3rd International Conference on Teaching and Computational Science (WTCS 2009), 345–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-11276-8_44.

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"A Geometric Approach to Ergodic Non-Homogeneous Markov Chains." In Wavelet Analysis and Multiresolution Methods, 355–80. CRC Press, 2000. http://dx.doi.org/10.1201/9781482290066-23.

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"Non-homogeneous Markov chains: Tail idempotents, tail sigma-fields and basis." In Proceedings of the Seventh Conference on Probability Theory, 269–82. De Gruyter, 1985. http://dx.doi.org/10.1515/9783112314036-030.

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"The theory of shuffling - an application of non-homogeneous Markov chains." In Probability Theory and Applications, 317–20. De Gruyter, 1987. http://dx.doi.org/10.1515/9783112314227-041.

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Fu, G., and D. Devaraj. "Non-homogeneous Markov Chain for bridge deterioration modeling." In Bridge Maintenance, Safety, Management and Life-Cycle Optimization, 154. CRC Press, 2010. http://dx.doi.org/10.1201/b10430-84.

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Rodrigues, Eliane R., Mario H. Tarumoto, and Guadalupe Tzintzun. "A Non-Homogeneous Markov Chain Model to Study Ozone Exceedances in Mexico City." In Current Air Quality Issues. InTech, 2015. http://dx.doi.org/10.5772/59728.

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Conference papers on the topic "Markov chains non-homogeneous"

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Reznicek, Jan, Martin Kohlik, and Hana Kubatova. "Non-Homogeneous Continuous Time Markov Chains Calculations." In 2020 23rd Euromicro Conference on Digital System Design (DSD). IEEE, 2020. http://dx.doi.org/10.1109/dsd51259.2020.00108.

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Reznicek, Jan, Martin Kohlik, and Hana Kubatova. "Accurate Inexact Calculations of Non-Homogeneous Markov Chains." In 2019 22nd Euromicro Conference on Digital System Design (DSD). IEEE, 2019. http://dx.doi.org/10.1109/dsd.2019.00074.

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Reznicek, Jan, Martin Kohlik, and Hana Kubatova. "Hierarchical Dependability Models based on Non-Homogeneous Continuous Time Markov Chains." In 2019 14th International Conference on Design & Technology of Integrated Systems In Nanoscale Era (DTIS). IEEE, 2019. http://dx.doi.org/10.1109/dtis.2019.8735006.

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Shaohua, Jin, Liu Huitao, Liu Jian, Wang Yongxue, and Zhang Jian. "Notice of Retraction: Many Strong Limit Theorems for Non-homogeneous Markov Chains Indexed by a Non-homogeneous Tree." In 2010 2nd International Workshop on Education Technology and Computer Science (ETCS). IEEE, 2010. http://dx.doi.org/10.1109/etcs.2010.346.

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Jin, Shaohua, Lu Yan, Yanping Wan, Shasha Jin, and Shuguang Sun. "Many Strong Deviation Theorems for Markov Chains on a Non-homogeneous Tree." In 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2010. http://dx.doi.org/10.1109/icicta.2010.192.

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Jin, Shaohua, Jian Liu, Nan Li, Yanping Wan, and Shuguang Sun. "Some strong limit theorems for Markov chains on a special non-homogeneous tree." In 2010 Sixth International Conference on Natural Computation (ICNC). IEEE, 2010. http://dx.doi.org/10.1109/icnc.2010.5582685.

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Sandels, Claes, Joakim Widen, and Lars Nordstrom. "Simulating occupancy in office buildings with non-homogeneous Markov chains for Demand Response analysis." In 2015 IEEE Power & Energy Society General Meeting. IEEE, 2015. http://dx.doi.org/10.1109/pesgm.2015.7285865.

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Palma, Jonathan M., Ceilia F. Morais, and Ricardo C. L. F. Oliveira. "$\mathscr{H}_{\infty}$ state-feedback gain-scheduled control for MJLS with non-homogeneous Markov chains*." In 2018 Annual American Control Conference (ACC). IEEE, 2018. http://dx.doi.org/10.23919/acc.2018.8430913.

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Shao-hua, Jin, Yan Lu, Ding Chong-guang, and Chen Wen-feng. "Some Strong Limit Theorems for Markov Chains of Continuous State Space on a Non-Homogeneous Tree." In 2009 Second International Conference on Intelligent Computation Technology and Automation. IEEE, 2009. http://dx.doi.org/10.1109/icicta.2009.780.

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Feng, Siwei, Yuki Itoh, Mario Parente, and Marco F. Duarte. "Tailoring non-homogeneous Markov chain wavelet models for hyperspectral signature classification." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7026046.

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