Littérature scientifique sur le sujet « Markov chain simulation »
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Articles de revues sur le sujet "Markov chain simulation":
BOUCHER, THOMAS R., et DAREN B. H. CLINE. « PIGGYBACKING THRESHOLD PROCESSES WITH A FINITE STATE MARKOV CHAIN ». Stochastics and Dynamics 09, no 02 (juin 2009) : 187–204. http://dx.doi.org/10.1142/s0219493709002622.
Bucklew, James A., Peter Ney et John S. Sadowsky. « Monte Carlo simulation and large deviations theory for uniformly recurrent Markov chains ». Journal of Applied Probability 27, no 1 (mars 1990) : 44–59. http://dx.doi.org/10.2307/3214594.
Bucklew, James A., Peter Ney et John S. Sadowsky. « Monte Carlo simulation and large deviations theory for uniformly recurrent Markov chains ». Journal of Applied Probability 27, no 01 (mars 1990) : 44–59. http://dx.doi.org/10.1017/s0021900200038419.
Chung, Gunhui, Kyu Bum Sim, Deok Jun Jo et Eung Seok Kim. « Hourly Precipitation Simulation Characteristic Analysis Using Markov Chain Model ». Journal of Korean Society of Hazard Mitigation 16, no 3 (30 juin 2016) : 351–57. http://dx.doi.org/10.9798/kosham.2016.16.3.351.
Glynn, Peter W., et Chang-Han Rhee. « Exact estimation for Markov chain equilibrium expectations ». Journal of Applied Probability 51, A (décembre 2014) : 377–89. http://dx.doi.org/10.1239/jap/1417528487.
Glynn, Peter W., et Chang-Han Rhee. « Exact estimation for Markov chain equilibrium expectations ». Journal of Applied Probability 51, A (décembre 2014) : 377–89. http://dx.doi.org/10.1017/s0021900200021392.
Jasra, Ajay, Kody J. H. Law et Yaxian Xu. « Markov chain simulation for multilevel Monte Carlo ». Foundations of Data Science 3, no 1 (2021) : 27. http://dx.doi.org/10.3934/fods.2021004.
Li, Weidong, Baoguo Li et Yuanchun Shi. « Markov-chain simulation of soil textural profiles ». Geoderma 92, no 1-2 (septembre 1999) : 37–53. http://dx.doi.org/10.1016/s0016-7061(99)00024-5.
Milios, Dimitrios, et Stephen Gilmore. « Markov Chain Simulation with Fewer Random Samples ». Electronic Notes in Theoretical Computer Science 296 (août 2013) : 183–97. http://dx.doi.org/10.1016/j.entcs.2013.07.012.
Skeel, Robert, et Youhan Fang. « Comparing Markov Chain Samplers for Molecular Simulation ». Entropy 19, no 10 (21 octobre 2017) : 561. http://dx.doi.org/10.3390/e19100561.
Thèses sur le sujet "Markov chain simulation":
Suzuki, Yuya. « Rare-event Simulation with Markov Chain Monte Carlo ». Thesis, KTH, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138950.
Gudmundsson, Thorbjörn. « Rare-event simulation with Markov chain Monte Carlo ». Doctoral thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157522.
QC 20141216
Fan, Yanan. « Efficient implementation of Markov chain Monte Carlo ». Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343307.
Cheal, Ryan. « Markov Chain Monte Carlo methods for simulation in pedigrees ». Thesis, University of Bath, 1996. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362254.
BALDIOTI, HUGO RIBEIRO. « MARKOV CHAIN MONTE CARLO FOR NATURAL INFLOW ENERGY SCENARIOS SIMULATION ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36058@1.
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE EXCELENCIA ACADEMICA
Constituído por uma matriz eletro-energética predominantemente hídrica e território de proporções continentais, o Brasil apresenta características únicas, sendo possível realizar o aproveitamento dos fartos recursos hídricos presentes no território nacional. Aproximadamente 65 por cento da capacidade de geração de energia elétrica advém de recursos hidrelétricos enquanto 28 por cento de recursos termelétricos. Sabe-se que regimes hidrológicos de vazões naturais são de natureza estocástica e em função disso é preciso tratá-los para que se possa planejar a operação do sistema, sendo assim, o despacho hidrotérmico é de suma importância e caracterizado por sua dependência estocástica. A partir das vazões naturais é possível calcular a Energia Natural Afluente (ENA) que será utilizada diretamente no processo de simulação de séries sintéticas que, por sua vez, são utilizadas no processo de otimização, responsável pelo cálculo da política ótima visando minimizar os custos de operação do sistema. Os estudos referentes a simulação de cenários sintéticos de ENA vêm se desenvolvendo com novas propostas metodológicas ao longo dos anos. Tais desenvolvimentos muitas vezes pressupõem Gaussianidade dos dados, de forma que seja possível ajustar uma distribuição paramétrica nos mesmos. Percebeu-se que na maioria dos casos reais, no contexto do Setor Elétrico Brasileiro, os dados não podem ser tratados desta forma, uma vez que apresentam em sua densidade comportamentos de cauda relevantes e uma acentuada assimetria. É necessário para o planejamento da operação do Sistema Interligado Nacional (SIN) que a assimetria intrínseca a este comportamento seja passível de reprodução. Dessa forma, este trabalho propõe duas abordagens não paramétricas para simulação de cenários. A primeira refere-se ao processo de amostragem dos resíduos das séries de ENA, para tanto, utiliza-se a técnica Markov Chain Monte Carlo (MCMC) e o Kernel Density Estimation. A segunda metodologia proposta aplica o MCMC Interconfigurações diretamente nas séries de ENA para simulação de cenários sintéticos a partir de uma abordagem inovadora para transição entre as matrizes e períodos. Os resultados da implementação das metodologias, observados graficamente e a partir de testes estatísticos de aderência ao histórico de dados, apontam que as propostas conseguem reproduzir com uma maior acurácia as características assimétricas sem perder a capacidade de reproduzir estatísticas básicas. Destarte, pode-se afirmar que os modelos propostos são boas alternativas em relação ao modelo vigente utilizado pelo setor elétrico brasileiro.
Consisting of an electro-energetic matrix with hydro predominance and a continental proportion territory, Brazil presents unique characteristics, being able to make use of the abundant water resources in the national territory. Approximately 65 percent of the electricity generation capacity comes from hydropower while 28 percent from thermoelectric plants. It is known that hydrological regimes have a stochastic nature and it is necessary to treat them so the energy system can be planned, thus the hydrothermal dispatch is extremely important and characterized by its stochastic dependence. From the natural streamflows it is possible to calculate the Natural Inflow Energy (NIE) that will be used directly in the synthetic series simulation process, which, in turn, are used on the optimization process, responsible for optimal policy calculation in order to minimize the system operational costs. The studies concerning the simulation of synthetic scenarios of NIE have been developing with new methodological proposals over the years. Such developments often presuppose data Gaussianity, so that a parametric distribution can be fitted to them. It was noticed that in the majority of real cases, in the context of the Brazilian Electrical Sector, the data cannot be treated like that, since they present in their density relevant tail behavior and skewness. It is necessary for the National Interconnected System (SIN) operational planning that the intrinsic skewness behavior is amenable to reproduction. Thus, this paper proposes two non-parametric approaches to scenarios simulation. The first one refers to the process of NIE series residues sampling, using a Markov Chain Monte Carlo (MCMC) technique and the Kernel Density Estimation. The second methodology is also proposed where the MCMC is applied periodically and directly in the NIE series to simulate synthetic scenarios using an innovative approach for transitions between matrices. The methodologies implementation results, observed graphically and based on statistical tests of adherence to the historical data, indicate that the proposals can reproduce with greater accuracy the asymmetric characteristics without losing the ability to reproduce basic statistics. Thus, one can conclude that the proposed models are good alternatives in relation to the current model of the Brazilian Electric Sector.
Mehl, Christopher. « Bayesian Hierarchical Modeling and Markov Chain Simulation for Chronic Wasting Disease ». Diss., University of Colorado at Denver, 2004. http://hdl.handle.net/10919/71563.
Zhou, Yi. « Simulation and Performance Analysis of Strategic Air Traffic Management under Weather Uncertainty ». Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc68071/.
Gudmundsson, Thorbjörn. « Markov chain Monte Carlo for rare-event simulation in heavy-tailed settings ». Licentiate thesis, KTH, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-134624.
Ren, Ruichao. « Accelerating Markov chain Monte Carlo simulation through sequential updating and parallel computing ». Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1428844711&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Pitt, Michael K. « Bayesian inference for non-Gaussian state space model using simulation ». Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389211.
Livres sur le sujet "Markov chain simulation":
Gamerman, Dani. Markov chain Monte Carlo : Stochastic simulation for Bayesian inference. London : Chapman & Hall, 1997.
Gamerman, Dani. Markov chain Monte Carlo : Stochastic simulation for Bayesian inference. 2e éd. Boca Raton : Taylor & Francis, 2006.
Gamerman, D. Markov chain Monte Carlo : Stochastic simulation for Bayesian inference. London : Chapman & Hall, 1997.
Jerrum, Mark. Uniform sampling modulo a group of symmetries using Markov chain simulation. Edinburgh : LFCS, Dept. of Computer Science, University of Edinburgh, 1993.
Cowles, Mary Kathryn. A simulation approach to convergence rates for Markov chain Monte Carlo algorithms. [Toronto] : University of Toronto, Dept. of Statistics, 1996.
Yücesan, Enver. Analysis of Markov chains using simulation graph models. Fontainebleau : INSEAD, 1990.
Brémaud, Pierre. Markov chains : Gibbs fields, Monte Carlo simulation, and queues. New York : Springer, 1999.
J, Stewart William. Probability, Markov chains, queues and simulation : The mathematical basis of performance modeling. Princeton : Princeton University Press, 2009.
Berg, Bernd A. Markov chain Monte Carlo simulations and their statistical analysis : With web-based Fortran code. Hackensack, NJ : World Scientific, 2004.
Berg, Bernard A. Markov chain Monte Carlo simulations and their statistical analysis : With web-based fortran code. Singapore : World Scientific Publishing, 2004.
Chapitres de livres sur le sujet "Markov chain simulation":
Li, Rongpeng, et Aiichiro Nakano. « Markov Chain, a Peek into the Future ». Dans Simulation with Python, 19–37. Berkeley, CA : Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8185-7_2.
DasGupta, Anirban. « Simulation and Markov Chain Monte Carlo ». Dans Springer Texts in Statistics, 613–87. New York, NY : Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9634-3_19.
Hautsch, Nikolaus, et Yangguoyi Ou. « Stochastic Volatility Estimation Using Markov Chain Simulation ». Dans Applied Quantitative Finance, 249–74. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-69179-2_12.
El Haddad, Rami, Joseph El Maalouf, Christian Lécot et Pierre L’Ecuyer. « Sudoku Latin Square Sampling for Markov Chain Simulation ». Dans Springer Proceedings in Mathematics & ; Statistics, 207–30. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43465-6_10.
Meenakshisundaram, Swaminathan, Anirudh Srikanth, Viswanath Kumar Ganesan, Natarajan Vijayarangan et Ananda Padmanaban Srinivas. « Forecasting : Bayesian Inference Using Markov Chain Monte Carlo Simulation ». Dans Smart Innovation, Systems and Technologies, 215–28. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5974-3_19.
Bhattacharya, Rabi, Lizhen Lin et Victor Patrangenaru. « Markov Chain Monte Carlo (MCMC) Simulation and Bayes Theory ». Dans Springer Texts in Statistics, 325–32. New York, NY : Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-4032-5_14.
Andrieu, Christophe, Arnaud Doucet et Roman Holenstein. « Particle Markov Chain Monte Carlo for Efficient Numerical Simulation ». Dans Monte Carlo and Quasi-Monte Carlo Methods 2008, 45–60. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04107-5_3.
Saini, Gurdeep, Naveen Yadav, Biju R. Mohan et Nagaraj Naik. « Time Series Forecasting Using Markov Chain Probability Transition Matrix with Genetic Algorithm Optimisation ». Dans Modeling, Simulation and Optimization, 439–51. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9829-6_34.
Rochani, Haresh, et Daniel F. Linder. « Markov Chain Monte-Carlo Methods for Missing Data Under Ignorability Assumptions ». Dans Monte-Carlo Simulation-Based Statistical Modeling, 129–42. Singapore : Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3307-0_7.
Glynn, Peter W., et Shane G. Henderson. « A Central Limit Theorem For Empirical Quantiles in the Markov Chain Setting ». Dans Advances in Modeling and Simulation, 211–38. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10193-9_11.
Actes de conférences sur le sujet "Markov chain simulation":
Li, Liang, Qi-sheng Guo et Xiu-yue Yang. « Evaluation method based on markov chain model ». Dans 2008 Asia Simulation Conference - 7th International Conference on System Simulation and Scientific Computing (ICSC). IEEE, 2008. http://dx.doi.org/10.1109/asc-icsc.2008.4675615.
Chan, Lay Guat, et Adriana Irawati Nur Binti Ibrahim. « Markov chain Monte Carlo simulation for Bayesian Hidden Markov Models ». Dans THE 4TH INTERNATIONAL CONFERENCE ON QUANTITATIVE SCIENCES AND ITS APPLICATIONS (ICOQSIA 2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4966059.
Glushkov, A. N., V. V. Menshikh, N. S. Khohlov, O. I. Bokova et M. Y. Kalinin. « Gaussian signals simulation using biconnected Markov chain ». Dans 2017 2nd International Ural Conference on Measurements (UralCon). IEEE, 2017. http://dx.doi.org/10.1109/uralcon.2017.8120718.
Zhang, Limao, Ronald Ekyalimpa, Stephen Hague, Michael Werner et Simaan AbouRizk. « Updating geological conditions using Bayes theorem and Markov chain ». Dans 2015 Winter Simulation Conference (WSC). IEEE, 2015. http://dx.doi.org/10.1109/wsc.2015.7408498.
Bazargan, Hamid, Mike Christie et Hamdi Tchelepi. « Efficient Markov Chain Monte Carlo Sampling Using Polynomial Chaos Expansion ». Dans SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, 2013. http://dx.doi.org/10.2118/163663-ms.
Suzuki, Yuya, et Thorbjörn Gudmundsson. « Markov Chain Monte Carlo for Risk Measures ». Dans 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SCITEPRESS - Science and Technology Publications, 2014. http://dx.doi.org/10.5220/0005035303300338.
Ruessink, B. G. « Application of Markov Chain simulation for model calibration ». Dans The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247007.
Israel, Wescott B., et John B. Ferris. « Developing Markov chain models for road surface simulation ». Dans Defense and Security Symposium, sous la direction de Kevin Schum et Dawn A. Trevisani. SPIE, 2007. http://dx.doi.org/10.1117/12.720066.
CHEN, Chun, Chao-hsin LIN et Qingyan CHEN. « Predicting Transient Particle Transport In Enclosed Environments Based On Markov Chain ». Dans 2017 Building Simulation Conference. IBPSA, 2013. http://dx.doi.org/10.26868/25222708.2013.1202.
Buist, Eric, Wyean Chan et Pierre L'Ecuyer. « Speeding up call center simulation and optimization by Markov chain uniformization ». Dans 2008 Winter Simulation Conference (WSC). IEEE, 2008. http://dx.doi.org/10.1109/wsc.2008.4736250.
Rapports d'organisations sur le sujet "Markov chain simulation":
Calvin, James M. Markov Chain Moment Formulas for Regenerative Simulation. Fort Belvoir, VA : Defense Technical Information Center, juin 1989. http://dx.doi.org/10.21236/ada210684.
Athreya, Krishna B., Hani Doss et Jayaram Sethuraman. A Proof of Convergence of the Markov Chain Simulation Method. Fort Belvoir, VA : Defense Technical Information Center, juillet 1992. http://dx.doi.org/10.21236/ada255456.
Glaser, R., G. Johannesson, S. Sengupta, B. Kosovic, S. Carle, G. Franz, R. Aines et al. Stochastic Engine Final Report : Applying Markov Chain Monte Carlo Methods with Importance Sampling to Large-Scale Data-Driven Simulation. Office of Scientific and Technical Information (OSTI), mars 2004. http://dx.doi.org/10.2172/15009813.
Krebs, William B. Markov Chain Simulations of Binary Matrices. Fort Belvoir, VA : Defense Technical Information Center, janvier 1992. http://dx.doi.org/10.21236/ada249265.