Literatura académica sobre el tema "Markov chain Monte Carlo samplers"
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Artículos de revistas sobre el tema "Markov chain Monte Carlo samplers"
South, L. F., A. N. Pettitt y C. C. Drovandi. "Sequential Monte Carlo Samplers with Independent Markov Chain Monte Carlo Proposals". Bayesian Analysis 14, n.º 3 (septiembre de 2019): 753–76. http://dx.doi.org/10.1214/18-ba1129.
Texto completoEveritt, Richard G., Richard Culliford, Felipe Medina-Aguayo y Daniel J. Wilson. "Sequential Monte Carlo with transformations". Statistics and Computing 30, n.º 3 (17 de noviembre de 2019): 663–76. http://dx.doi.org/10.1007/s11222-019-09903-y.
Texto completoXiaopeng Xu, Xiaopeng Xu, Chuancai Liu Xiaopeng Xu, Hongji Yang Chuancai Liu y Xiaochun Zhang Hongji Yang. "A Multi-Trajectory Monte Carlo Sampler". 網際網路技術學刊 23, n.º 5 (septiembre de 2022): 1117–28. http://dx.doi.org/10.53106/160792642022092305020.
Texto completoDellaportas, Petros y Ioannis Kontoyiannis. "Control variates for estimation based on reversible Markov chain Monte Carlo samplers". Journal of the Royal Statistical Society: Series B (Statistical Methodology) 74, n.º 1 (3 de noviembre de 2011): 133–61. http://dx.doi.org/10.1111/j.1467-9868.2011.01000.x.
Texto completoJones, Galin L., Gareth O. Roberts y Jeffrey S. Rosenthal. "Convergence of Conditional Metropolis-Hastings Samplers". Advances in Applied Probability 46, n.º 2 (junio de 2014): 422–45. http://dx.doi.org/10.1239/aap/1401369701.
Texto completoJones, Galin L., Gareth O. Roberts y Jeffrey S. Rosenthal. "Convergence of Conditional Metropolis-Hastings Samplers". Advances in Applied Probability 46, n.º 02 (junio de 2014): 422–45. http://dx.doi.org/10.1017/s0001867800007151.
Texto completoLevy, Roy. "The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling". Journal of Probability and Statistics 2009 (2009): 1–18. http://dx.doi.org/10.1155/2009/537139.
Texto completoKilic, Zeliha, Max Schweiger, Camille Moyer y Steve Pressé. "Monte Carlo samplers for efficient network inference". PLOS Computational Biology 19, n.º 7 (18 de julio de 2023): e1011256. http://dx.doi.org/10.1371/journal.pcbi.1011256.
Texto completoGuha, Subharup, Steven N. MacEachern y Mario Peruggia. "Benchmark Estimation for Markov chain Monte Carlo Samples". Journal of Computational and Graphical Statistics 13, n.º 3 (septiembre de 2004): 683–701. http://dx.doi.org/10.1198/106186004x2598.
Texto completoSiems, Tobias. "Markov Chain Monte Carlo on finite state spaces". Mathematical Gazette 104, n.º 560 (18 de junio de 2020): 281–87. http://dx.doi.org/10.1017/mag.2020.51.
Texto completoTesis sobre el tema "Markov chain Monte Carlo samplers"
Guha, Subharup. "Benchmark estimation for Markov Chain Monte Carlo samplers". The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1085594208.
Texto completoSisson, Scott Antony. "Markov chains for genetics and extremes". Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391095.
Texto completoPang, Wan-Kai. "Modelling ordinal categorical data : a Gibbs sampler approach". Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323876.
Texto completoVerhelst, Norman D., Reinhold Hatzinger y Patrick Mair. "The Rasch Sampler". Foundation for Open Access Statistics, 2007. http://dx.doi.org/10.18637/jss.v020.i04.
Texto completoZhu, Qingyun. "Product Deletion and Supply Chain Management". Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/527.
Texto completoAl, Hakmani Rahab. "Bayesian Estimation of Mixture IRT Models using NUTS". OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1641.
Texto completoLu, Pingbo. "Calibrated Bayes factors for model selection and model averaging". The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343396705.
Texto completoDeng, Wei. "Multiple imputation for marginal and mixed models in longitudinal data with informative missingness". Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1126890027.
Texto completoTitle from first page of PDF file. Document formatted into pages; contains xiii, 108 p.; also includes graphics. Includes bibliographical references (p. 104-108). Available online via OhioLINK's ETD Center
Wu, Yi-Fang. "Accuracy and variability of item parameter estimates from marginal maximum a posteriori estimation and Bayesian inference via Gibbs samplers". Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/5879.
Texto completoFu, Shuting. "Bayesian Logistic Regression Model with Integrated Multivariate Normal Approximation for Big Data". Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/451.
Texto completoLibros sobre el tema "Markov chain Monte Carlo samplers"
Liang, F. Advanced Markov chain Monte Carlo methods: Learning from past samples. Hoboken, NJ: Wiley, 2010.
Buscar texto completoHandbook for Markov chain Monte Carlo. Boca Raton: Taylor & Francis, 2011.
Buscar texto completoLiang, Faming, Chuanhai Liu y Raymond J. Carroll. Advanced Markov Chain Monte Carlo Methods. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470669723.
Texto completoR, Gilks W., Richardson S y Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. Boca Raton, Fla: Chapman & Hall, 1998.
Buscar texto completoR, Gilks W., Richardson S y Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. London: Chapman & Hall, 1996.
Buscar texto completoS, Kendall W., Liang F. 1970- y Wang J. S. 1960-, eds. Markov chain Monte Carlo: Innovations and applications. Singapore: World Scientific, 2005.
Buscar texto completoJoseph, Anosh. Markov Chain Monte Carlo Methods in Quantum Field Theories. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46044-0.
Texto completoGamerman, Dani. Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. London: Chapman & Hall, 1997.
Buscar texto completoFreitas, Lopes Hedibert, ed. Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. 2a ed. Boca Raton: Taylor & Francis, 2006.
Buscar texto completoMarkov chain Monte Carlo: Stochastic simulation for Bayesian inference. London: Chapman & Hall, 1997.
Buscar texto completoCapítulos de libros sobre el tema "Markov chain Monte Carlo samplers"
Keith, Jonathan M. y Christian M. Davey. "Bayesian Approaches to the Design of Markov Chain Monte Carlo Samplers". En Monte Carlo and Quasi-Monte Carlo Methods 2012, 455–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41095-6_22.
Texto completoAoki, Satoshi, Hisayuki Hara y Akimichi Takemura. "Markov Chain Monte Carlo Methods over Discrete Sample Space". En Springer Series in Statistics, 23–31. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3719-2_2.
Texto completoTanner, Martin A. "Markov Chain Monte Carlo: The Gibbs Sampler and the Metropolis Algorithm". En Tools for Statistical Inference, 137–92. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4024-2_6.
Texto completoKitchen, Nathan y Andreas Kuehlmann. "A Markov Chain Monte Carlo Sampler for Mixed Boolean/Integer Constraints". En Computer Aided Verification, 446–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02658-4_34.
Texto completoTanner, Martin A. "Markov Chain Monte Carlo: The Gibbs Sampler and the Metropolis Algorithm". En Tools for Statistical Inference, 102–46. New York, NY: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4684-0192-9_6.
Texto completoYu, Thomas, Marco Pizzolato, Gabriel Girard, Jonathan Rafael-Patino, Erick Jorge Canales-Rodríguez y Jean-Philippe Thiran. "Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler". En Lecture Notes in Computer Science, 818–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20351-1_64.
Texto completoSorensen, Daniel y Daniel Gianola. "Markov Chain Monte Carlo". En Statistics for Biology and Health, 497–537. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/0-387-22764-4_11.
Texto completoWedel, Michel y Peter Lenk. "Markov Chain Monte Carlo". En Encyclopedia of Operations Research and Management Science, 925–30. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1164.
Texto completoJoseph, Anosh. "Markov Chain Monte Carlo". En SpringerBriefs in Physics, 37–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46044-0_4.
Texto completoFürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin et al. "Markov Chain Monte Carlo". En Encyclopedia of Machine Learning, 639–42. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_511.
Texto completoActas de conferencias sobre el tema "Markov chain Monte Carlo samplers"
Vaiciulyte, Ingrida. "Adaptive Monte-Carlo Markov chain for multivariate statistical estimation". En International Workshop of "Stochastic Programming for Implementation and Advanced Applications". The Association of Lithuanian Serials, 2012. http://dx.doi.org/10.5200/stoprog.2012.21.
Texto completoBURKETT, K. M., B. McNENEY y J. GRAHAM. "A MARKOV CHAIN MONTE CARLO SAMPLER FOR GENE GENEALOGIES CONDITIONAL ON HAPLOTYPE DATA". En Proceedings of Statistics 2011 Canada/IMST 2011-FIM XX. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814417983_0003.
Texto completoGuzman, Rel. "Monte Carlo Methods on High Dimensional Data". En LatinX in AI at Neural Information Processing Systems Conference 2018. Journal of LatinX in AI Research, 2018. http://dx.doi.org/10.52591/lxai2018120314.
Texto completoPutze, A., L. Derome, F. Donato y D. Maurin. "A Markov Chain Monte Carlo technique to sample transport and source parameters of Galactic cosmic rays". En Proceedings of the 12th ICATPP Conference. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814329033_0056.
Texto completoDavis, Gary A. "Sample-Based Estimation of Vehicle Speeds from Yaw Marks: Bayesian Implementation Using Markov Chain Monte Carlo Simulation". En SAE 2014 World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2014. http://dx.doi.org/10.4271/2014-01-0467.
Texto completoDe Sa, Christopher, Kunle Olukotun y Christopher Ré. "Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling". En Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/672.
Texto completoLin, Pin-Yi y Kuei-Yuan Chan. "Optimal Sample Augmentation and Resource Allocation for Design With Inadequate Uncertainty Data". En ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70234.
Texto completoAn, Dawn y Joo-Ho Choi. "Improved MCMC Method for Parameter Estimation Based on Marginal Probability Density Function". En ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48784.
Texto completoWu, Y. T., A. P. Ku y C. M. Serratella. "A Robust and Efficient Computational Method for Fatigue Reliability Update Using Inspected Data". En ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/omae2009-80034.
Texto completoBérešová, Simona. "Numerical realization of the Bayesian inversion accelerated using surrogate models". En Programs and Algorithms of Numerical Mathematics 21. Institute of Mathematics, Czech Academy of Sciences, 2023. http://dx.doi.org/10.21136/panm.2022.03.
Texto completoInformes sobre el tema "Markov chain Monte Carlo samplers"
Safta, Cosmin, Mohammad Khalil y Habib N. Najm. Transitional Markov Chain Monte Carlo Sampler in UQTk. Office of Scientific and Technical Information (OSTI), marzo de 2020. http://dx.doi.org/10.2172/1606084.
Texto completoGelfand, Alan E. y Sujit K. Sahu. On Markov Chain Monte Carlo Acceleration. Fort Belvoir, VA: Defense Technical Information Center, abril de 1994. http://dx.doi.org/10.21236/ada279393.
Texto completoWarnes, Gregory R. HYDRA: A Java Library for Markov Chain Monte Carlo. Fort Belvoir, VA: Defense Technical Information Center, marzo de 2002. http://dx.doi.org/10.21236/ada459649.
Texto completoBates, Cameron Russell y Edward Allen Mckigney. Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library. Office of Scientific and Technical Information (OSTI), enero de 2018. http://dx.doi.org/10.2172/1417145.
Texto completoBaltz, E. Markov Chain Monte Carlo Exploration of Minimal Supergravity with Implications for Dark Matter. Office of Scientific and Technical Information (OSTI), julio de 2004. http://dx.doi.org/10.2172/827306.
Texto completoSethuraman, Jayaram. Easily Verifiable Conditions for the Convergence of the Markov Chain Monte Carlo Method. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 1995. http://dx.doi.org/10.21236/ada308874.
Texto completoDoss, Hani. Studies in Reliability Theory and Survival Analysis and in Markov Chain Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1998. http://dx.doi.org/10.21236/ada367895.
Texto completoDoss, Hani. Statistical Inference for Coherent Systems from Partial Information and Markov Chain Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, enero de 1996. http://dx.doi.org/10.21236/ada305676.
Texto completoDoss, Hani. Studies in Reliability Theory and Survival Analysis and in Markov Chain Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, diciembre de 1998. http://dx.doi.org/10.21236/ada379998.
Texto completoKnopp, Jeremy S. y Fumio Kojima. Inverse Problem for Electromagnetic Propagation in a Dielectric Medium using Markov Chain Monte Carlo Method (Preprint). Fort Belvoir, VA: Defense Technical Information Center, agosto de 2012. http://dx.doi.org/10.21236/ada565876.
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