Bibliografías temáticas / Convergence of Markov processes

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Literatura académica sobre el tema "Convergence of Markov processes"

Autor: Grafiati
Publicado: 23 de noviembre de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Convergence of Markov processes"

1

Abakuks, A., S. N. Ethier, and T. G. Kurtz. "Markov Processes: Characterization and Convergence." Biometrics 43, no. 2 (1987): 484. http://dx.doi.org/10.2307/2531839.

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Perkins, Edwin, S. N. Ethier, and T. G. Kurtz. "Markov Processes, Characterization and Convergence." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 2 (1988): 367. http://dx.doi.org/10.2307/2982773.

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Franz, Uwe, Volkmar Liebscher, and Stefan Zeiser. "Piecewise-Deterministic Markov Processes as Limits of Markov Jump Processes." Advances in Applied Probability 44, no. 3 (2012): 729–48. http://dx.doi.org/10.1239/aap/1346955262.

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A classical result about Markov jump processes states that a certain class of dynamical systems given by ordinary differential equations are obtained as the limit of a sequence of scaled Markov jump processes. This approach fails if the scaling cannot be carried out equally across all entities. In the present paper we present a convergence theorem for such an unequal scaling. In contrast to an equal scaling the limit process is not purely deterministic but still possesses randomness. We show that these processes constitute a rich subclass of piecewise-deterministic processes. Such processes ap
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Franz, Uwe, Volkmar Liebscher, and Stefan Zeiser. "Piecewise-Deterministic Markov Processes as Limits of Markov Jump Processes." Advances in Applied Probability 44, no. 03 (2012): 729–48. http://dx.doi.org/10.1017/s0001867800005851.

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A classical result about Markov jump processes states that a certain class of dynamical systems given by ordinary differential equations are obtained as the limit of a sequence of scaled Markov jump processes. This approach fails if the scaling cannot be carried out equally across all entities. In the present paper we present a convergence theorem for such an unequal scaling. In contrast to an equal scaling the limit process is not purely deterministic but still possesses randomness. We show that these processes constitute a rich subclass of piecewise-deterministic processes. Such processes ap
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5

Swishchuk, Anatoliy, and M. Shafiqul Islam. "Diffusion Approximations of the Geometric Markov Renewal Processes and Option Price Formulas." International Journal of Stochastic Analysis 2010 (December 19, 2010): 1–21. http://dx.doi.org/10.1155/2010/347105.

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We consider the geometric Markov renewal processes as a model for a security market and study this processes in a diffusion approximation scheme. Weak convergence analysis and rates of convergence of ergodic geometric Markov renewal processes in diffusion scheme are presented. We present European call option pricing formulas in the case of ergodic, double-averaged, and merged diffusion geometric Markov renewal processes.
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Aldous, David J. "Book Review: Markov processes: Characterization and convergence." Bulletin of the American Mathematical Society 16, no. 2 (1987): 315–19. http://dx.doi.org/10.1090/s0273-0979-1987-15533-9.

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Crank, Keith N., and Prem S. Puri. "A method of approximating Markov jump processes." Advances in Applied Probability 20, no. 1 (1988): 33–58. http://dx.doi.org/10.2307/1427269.

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We present a method of approximating Markov jump processes which was used by Fuhrmann [7] in a special case. We generalize the method and prove weak convergence results under mild assumptions. In addition we obtain bounds on the rates of convergence of the probabilities at arbitrary fixed times. The technique is demonstrated using a state-dependent branching process as an example.
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Crank, Keith N., and Prem S. Puri. "A method of approximating Markov jump processes." Advances in Applied Probability 20, no. 01 (1988): 33–58. http://dx.doi.org/10.1017/s0001867800017936.

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We present a method of approximating Markov jump processes which was used by Fuhrmann [7] in a special case. We generalize the method and prove weak convergence results under mild assumptions. In addition we obtain bounds on the rates of convergence of the probabilities at arbitrary fixed times. The technique is demonstrated using a state-dependent branching process as an example.
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Deng, Chang-Song, René L. Schilling, and Yan-Hong Song. "Subgeometric rates of convergence for Markov processes under subordination." Advances in Applied Probability 49, no. 1 (2017): 162–81. http://dx.doi.org/10.1017/apr.2016.83.

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Abstract We are interested in the rate of convergence of a subordinate Markov process to its invariant measure. Given a subordinator and the corresponding Bernstein function (Laplace exponent), we characterize the convergence rate of the subordinate Markov process; the key ingredients are the rate of convergence of the original process and the (inverse of the) Bernstein function. At a technical level, the crucial point is to bound three types of moment (subexponential, algebraic, and logarithmic) for subordinators as time t tends to ∞. We also discuss some concrete models and we show that subo
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Lam, Hoang-Chuong. "Weak Convergence for Markov Processes on Discrete State Spaces." Markov Processes And Related Fields, no. 2024 № 4 (30) (February 8, 2025): 587–98. https://doi.org/10.61102/1024-2953-mprf.2024.30.4.004.

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This study investigates the weak convergence of Markov processes on discrete state spaces under the assumption that the transition intensities converge to a constant. Additionally, the research determines the limits of higher-order moments of the Markov process, which are utilized to prove the existence of limit theorems.
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Tesis sobre el tema "Convergence of Markov processes"

1

Hahn, Léo. "Interacting run-and-tumble particles as piecewise deterministic Markov processes : invariant distribution and convergence." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2024. http://www.theses.fr/2024UCFA0084.

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Cette thèse étudie le comportement en temps long des particules run-and-tumble (RTPs), un modèle pour les bactéries en physique statistique hors équilibre, en utilisant des processus de Markov déterministes par morceaux (PDMPs). La motivation est d'améliorer la compréhension au niveau particulaire des phénomènes actifs, en particulier la séparation de phase induite par la motilité (MIPS). La mesure invariante pour deux RTPs avec jamming sur un tore 1D est déterminée pour mécanismes de tumble et jamming généraux, révélant deux classes d'universalité hors équilibre. De plus, la dépendance du tem
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2

Pötzelberger, Klaus. "On the Approximation of finite Markov-exchangeable processes by mixtures of Markov Processes." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1991. http://epub.wu.ac.at/526/1/document.pdf.

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We give an upper bound for the norm distance of (0,1) -valued Markov-exchangeable random variables to mixtures of distributions of Markov processes. A Markov-exchangeable random variable has a distribution that depends only on the starting value and the number of transitions 0-0, 0-1, 1-0 and 1-1. We show that if, for increasing length of variables, the norm distance to mixtures of Markov processes goes to 0, the rate of this convergence may be arbitrarily slow. (author's abstract)<br>Series: Forschungsberichte / Institut für Statistik
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3

Drozdenko, Myroslav. "Weak Convergence of First-Rare-Event Times for Semi-Markov Processes." Doctoral thesis, Västerås : Mälardalen University, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-394.

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Yuen, Wai Kong. "Application of geometric bounds to convergence rates of Markov chains and Markov processes on R[superscript]n." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ58619.pdf.

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Kaijser, Thomas. "Convergence in distribution for filtering processes associated to Hidden Markov Models with densities." Linköpings universitet, Matematik och tillämpad matematik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-92590.

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A Hidden Markov Model generates two basic stochastic processes, a Markov chain, which is hidden, and an observation sequence. The filtering process of a Hidden Markov Model is, roughly speaking, the sequence of conditional distributions of the hidden Markov chain that is obtained as new observations are received. It is well-known, that the filtering process itself, is also a Markov chain. A classical, theoretical problem is to find conditions which implies that the distributions of the filtering process converge towards a unique limit measure. This problem goes back to a paper of D Blackwell f
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6

Lachaud, Béatrice. "Détection de la convergence de processus de Markov." Phd thesis, Université René Descartes - Paris V, 2005. http://tel.archives-ouvertes.fr/tel-00010473.

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Notre travail porte sur le phénomène de cutoff pour des n-échantillons de processus de Markov, dans le but de l'appliquer à la détection de la convergence d'algorithmes parallélisés. Dans un premier temps, le processus échantillonné est un processus d'Ornstein-Uhlenbeck. Nous mettons en évidence le phénomène de cutoff pour le n-échantillon, puis nous faisons le lien avec la convergence en loi du temps d'atteinte par le processus moyen d'un niveau fixé. Dans un second temps, nous traitons le cas général où le processus échantillonné converge à vitesse exponentielle vers sa loi stationnaire. Nou
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7

Fisher, Diana. "Convergence analysis of MCMC method in the study of genetic linkage with missing data." Huntington, WV : [Marshall University Libraries], 2005. http://www.marshall.edu/etd/descript.asp?ref=568.

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Wang, Xinyu. "Sur la convergence sous-exponentielle de processus de Markov." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2012. http://tel.archives-ouvertes.fr/tel-00840858.

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Ma thèse de doctorat se concentre principalement sur le comportement en temps long des processus de Markov, les inégalités fonctionnelles et les techniques relatives. Plus spécifiquement, Je vais présenter les taux de convergence sous-exponentielle explicites des processus de Markov dans deux approches : la méthode Meyn-Tweedie et l'hypocoercivité (faible). Le document se divise en trois parties. Dans la première partie, Je vais présenter quelques résultats importants et des connaissances connexes. D'abord, un aperçu de mon domaine de recherche sera donné. La convergence exponentielle (ou sous
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Bouguet, Florian. "Étude quantitative de processus de Markov déterministes par morceaux issus de la modélisation." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S040/document.

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L'objet de cette thèse est d'étudier une certaine classe de processus de Markov, dits déterministes par morceaux, ayant de très nombreuses applications en modélisation. Plus précisément, nous nous intéresserons à leur comportement en temps long et à leur vitesse de convergence à l'équilibre lorsqu'ils admettent une mesure de probabilité stationnaire. L'un des axes principaux de ce manuscrit de thèse est l'obtention de bornes quantitatives fines sur cette vitesse, obtenues principalement à l'aide de méthodes de couplage. Le lien sera régulièrement fait avec d'autres domaines des mathématiques d
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Chotard, Alexandre. "Markov chain Analysis of Evolution Strategies." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112230/document.

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Cette thèse contient des preuves de convergence ou de divergence d'algorithmes d'optimisation appelés stratégies d'évolution (ESs), ainsi que le développement d'outils mathématiques permettant ces preuves.Les ESs sont des algorithmes d'optimisation stochastiques dits ``boîte noire'', i.e. où les informations sur la fonction optimisée se réduisent aux valeurs qu'elle associe à des points. En particulier, le gradient de la fonction est inconnu. Des preuves de convergence ou de divergence de ces algorithmes peuvent être obtenues via l'analyse de chaînes de Markov sous-jacentes à ces algorithmes.
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Libros sobre el tema "Convergence of Markov processes"

1

G, Kurtz Thomas, ed. Markov processes: Characterization and convergence. Wiley, 1986.

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Roberts, Gareth O. Convergence of slice sampler Markov chains. University of Toronto, 1997.

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Baxter, John Robert. Rates of convergence for everywhere-positive markov chains. University of Toronto, Dept. of Statistics, 1994.

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Roberts, Gareth O. Quantitative bounds for convergence rates of continuous time Markov processes. University of Toronto, Dept. of Statistics, 1996.

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Yuen, Wai Kong. Applications of Cheeger's constant to the convergence rate of Markov chains on Rn. University of Toronto, Dept. of Statistics, 1997.

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Roberts, Gareth O. On convergence rates of Gibbs samplers for uniform distributions. University of Toronto, 1997.

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Cowles, Mary Kathryn. Possible biases induced by MCMC convergence diagnostics. University of Toronto, Dept. of Statistics, 1997.

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Cowles, Mary Kathryn. A simulation approach to convergence rates for Markov chain Monte Carlo algorithms. University of Toronto, Dept. of Statistics, 1996.

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Wirsching, Günther J. The dynamical system generated by the 3n + 1 function. Springer, 1998.

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Petrone, Sonia. A note on convergence rates of Gibbs sampling for nonparametric mixtures. University of Toronto, Dept. of Statistics, 1998.

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Capítulos de libros sobre el tema "Convergence of Markov processes"

1

Zhang, Hanjun, Qixiang Mei, Xiang Lin, and Zhenting Hou. "Convergence Property of Standard Transition Functions." In Markov Processes and Controlled Markov Chains. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4613-0265-0_4.

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Altman, Eitan. "Convergence of discounted constrained MDPs." In Constrained Markov Decision Processes. Routledge, 2021. http://dx.doi.org/10.1201/9781315140223-17.

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Altman, Eitan. "Convergence as the horizon tends to infinity." In Constrained Markov Decision Processes. Routledge, 2021. http://dx.doi.org/10.1201/9781315140223-18.

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Kersting, G., and F. C. Klebaner. "Explosions in Markov Processes and Submartingale Convergence." In Athens Conference on Applied Probability and Time Series Analysis. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0749-8_9.

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Cai, Yuzhi. "How Rates of Convergence for Gibbs Fields Depend on the Interaction and the Kind of Scanning Used." In Markov Processes and Controlled Markov Chains. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4613-0265-0_31.

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Bernou, Armand. "On Subexponential Convergence to Equilibrium of Markov Processes." In Lecture Notes in Mathematics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96409-2_5.

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Pop-Stojanovic, Z. R. "Convergence in Energy and the Sector Condition for Markov Processes." In Seminar on Stochastic Processes, 1984. Birkhäuser Boston, 1986. http://dx.doi.org/10.1007/978-1-4684-6745-1_10.

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Feng, Jin, and Thomas Kurtz. "Large deviations for Markov processes and nonlinear semigroup convergence." In Mathematical Surveys and Monographs. American Mathematical Society, 2006. http://dx.doi.org/10.1090/surv/131/05.

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Chatterjee, Krishnendu, Mahdi JafariRaviz, Raimundo Saona, and Jakub Svoboda. "Value Iteration with Guessing for Markov Chains and Markov Decision Processes." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90653-4_11.

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Abstract Two standard models for probabilistic systems are Markov chains (MCs) and Markov decision processes (MDPs). Classic objectives for such probabilistic models for control and planning problems are reachability and stochastic shortest path. The widely studied algorithmic approach for these problems is the Value Iteration (VI) algorithm which iteratively applies local updates called Bellman updates. There are many practical approaches for VI in the literature but they all require exponentially many Bellman updates for MCs in the worst case. A preprocessing step is an algorithm that is dis
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Negoro, Akira, and Masaaki Tsuchiya. "Convergence and uniqueness theorems for markov processes associated with Lévy operators." In Lecture Notes in Mathematics. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/bfb0078492.

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Actas de conferencias sobre el tema "Convergence of Markov processes"

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Saldi, Naci, Sina Sanjari, and Serdar Yüksel. "Quantum Markov Decision Processes." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886823.

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Majeed, Sultan Javed, and Marcus Hutter. "On Q-learning Convergence for Non-Markov Decision Processes." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/353.

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Temporal-difference (TD) learning is an attractive, computationally efficient framework for model- free reinforcement learning. Q-learning is one of the most widely used TD learning technique that enables an agent to learn the optimal action-value function, i.e. Q-value function. Contrary to its widespread use, Q-learning has only been proven to converge on Markov Decision Processes (MDPs) and Q-uniform abstractions of finite-state MDPs. On the other hand, most real-world problems are inherently non-Markovian: the full true state of the environment is not revealed by recent observations. In th
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Amiri, Mohsen, and Sindri Magnússon. "On the Convergence of TD-Learning on Markov Reward Processes with Hidden States." In 2024 European Control Conference (ECC). IEEE, 2024. http://dx.doi.org/10.23919/ecc64448.2024.10591108.

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Shi, Chongyang, Yuheng Bu, and Jie Fu. "Information-Theoretic Opacity-Enforcement in Markov Decision Processes." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/749.

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The paper studies information-theoretic opacity, an information-flow privacy property, in a setting involving two agents: A planning agent who controls a stochastic system and an observer who partially observes the system states. The goal of the observer is to infer some secret, represented by a random variable, from its partial observations, while the goal of the planning agent is to make the secret maximally opaque to the observer while achieving a satisfactory total return. Modeling the stochastic system using a Markov decision process, two classes of opacity properties are considered---Las
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Ding, Dongsheng, Kaiqing Zhang, Tamer Basar, and Mihailo R. Jovanovic. "Convergence and optimality of policy gradient primal-dual method for constrained Markov decision processes." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867805.

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Ferreira Salvador, Paulo J., and Rui J. M. T. Valadas. "Framework based on Markov modulated Poisson processes for modeling traffic with long-range dependence." In ITCom 2001: International Symposium on the Convergence of IT and Communications, edited by Robert D. van der Mei and Frank Huebner-Szabo de Bucs. SPIE, 2001. http://dx.doi.org/10.1117/12.434317.

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Takagi, Hideaki, Muneo Kitajima, Tetsuo Yamamoto, and Yongbing Zhang. "Search process evaluation for a hierarchical menu system by Markov chains." In ITCom 2001: International Symposium on the Convergence of IT and Communications, edited by Robert D. van der Mei and Frank Huebner-Szabo de Bucs. SPIE, 2001. http://dx.doi.org/10.1117/12.434312.

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Hongbin Liang, Lin X. Cai, Hangguan Shan, Xuemin Shen, and Daiyuan Peng. "Adaptive resource allocation for media services based on semi-Markov decision process." In 2010 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2010. http://dx.doi.org/10.1109/ictc.2010.5674663.

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Tayeb, Shahab, Miresmaeil Mirnabibaboli, and Shahram Latifi. "Load Balancing in WSNs using a Novel Markov Decision Process Based Routing Algorithm." In 2016 6th International Conference on IT Convergence and Security (ICITCS). IEEE, 2016. http://dx.doi.org/10.1109/icitcs.2016.7740350.

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Chanron, Vincent, and Kemper Lewis. "A Study of Convergence in Decentralized Design." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dac-48782.

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The decomposition and coordination of decisions in the design of complex engineering systems is a great challenge. Companies who design these systems routinely allocate design responsibility of the various subsystems and components to different people, teams or even suppliers. The mechanisms behind this network of decentralized design decisions create difficult management and coordination issues. However, developing efficient design processes is paramount, especially with market pressures and customer expectations. Standard techniques to modeling and solving decentralized design problems typic
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Informes sobre el tema "Convergence of Markov processes"

1

Adler, Robert J., Stamatis Gambanis, and Gennady Samorodnitsky. On Stable Markov Processes. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada192892.

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Athreya, Krishna B., Hani Doss, and Jayaram Sethuraman. A Proof of Convergence of the Markov Chain Simulation Method. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada255456.

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Abdel-Hameed, M. Markovian Shock Models, Deterioration Processes, Stratified Markov Processes Replacement Policies. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada174646.

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Newell, Alan. Markovian Shock Models, Deterioration Processes, Stratified Markov Processes and Replacement Policies. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada174995.

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Cinlar, E. Markov Processes Applied to Control, Reliability and Replacement. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada208634.

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Rohlicek, J. R., and A. S. Willsky. Structural Decomposition of Multiple Time Scale Markov Processes,. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada189739.

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Serfozo, Richard F. Poisson Functionals of Markov Processes and Queueing Networks. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada191217.

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Serfozo, R. F. Poisson Functionals of Markov Processes and Queueing Networks,. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada194289.

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Draper, Bruce A., and J. Ross Beveridge. Learning to Populate Geospatial Databases via Markov Processes. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada374536.

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Sethuraman, Jayaram. Easily Verifiable Conditions for the Convergence of the Markov Chain Monte Carlo Method. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada308874.

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