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Статті в журналах з теми "Markov chain Monte Carlo samplers"
South, L. F., A. N. Pettitt, and C. C. Drovandi. "Sequential Monte Carlo Samplers with Independent Markov Chain Monte Carlo Proposals." Bayesian Analysis 14, no. 3 (September 2019): 753–76. http://dx.doi.org/10.1214/18-ba1129.
Повний текст джерелаEveritt, Richard G., Richard Culliford, Felipe Medina-Aguayo, and Daniel J. Wilson. "Sequential Monte Carlo with transformations." Statistics and Computing 30, no. 3 (November 17, 2019): 663–76. http://dx.doi.org/10.1007/s11222-019-09903-y.
Повний текст джерелаXiaopeng Xu, Xiaopeng Xu, Chuancai Liu Xiaopeng Xu, Hongji Yang Chuancai Liu, and Xiaochun Zhang Hongji Yang. "A Multi-Trajectory Monte Carlo Sampler." 網際網路技術學刊 23, no. 5 (September 2022): 1117–28. http://dx.doi.org/10.53106/160792642022092305020.
Повний текст джерелаDellaportas, Petros, and 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, no. 1 (November 3, 2011): 133–61. http://dx.doi.org/10.1111/j.1467-9868.2011.01000.x.
Повний текст джерелаJones, Galin L., Gareth O. Roberts, and Jeffrey S. Rosenthal. "Convergence of Conditional Metropolis-Hastings Samplers." Advances in Applied Probability 46, no. 2 (June 2014): 422–45. http://dx.doi.org/10.1239/aap/1401369701.
Повний текст джерелаJones, Galin L., Gareth O. Roberts, and Jeffrey S. Rosenthal. "Convergence of Conditional Metropolis-Hastings Samplers." Advances in Applied Probability 46, no. 02 (June 2014): 422–45. http://dx.doi.org/10.1017/s0001867800007151.
Повний текст джерелаLevy, 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.
Повний текст джерелаKilic, Zeliha, Max Schweiger, Camille Moyer, and Steve Pressé. "Monte Carlo samplers for efficient network inference." PLOS Computational Biology 19, no. 7 (July 18, 2023): e1011256. http://dx.doi.org/10.1371/journal.pcbi.1011256.
Повний текст джерелаGuha, Subharup, Steven N. MacEachern, and Mario Peruggia. "Benchmark Estimation for Markov chain Monte Carlo Samples." Journal of Computational and Graphical Statistics 13, no. 3 (September 2004): 683–701. http://dx.doi.org/10.1198/106186004x2598.
Повний текст джерелаSiems, Tobias. "Markov Chain Monte Carlo on finite state spaces." Mathematical Gazette 104, no. 560 (June 18, 2020): 281–87. http://dx.doi.org/10.1017/mag.2020.51.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаSisson, Scott Antony. "Markov chains for genetics and extremes." Thesis, University of Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391095.
Повний текст джерелаPang, 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.
Повний текст джерелаVerhelst, Norman D., Reinhold Hatzinger, and Patrick Mair. "The Rasch Sampler." Foundation for Open Access Statistics, 2007. http://dx.doi.org/10.18637/jss.v020.i04.
Повний текст джерелаZhu, Qingyun. "Product Deletion and Supply Chain Management." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/527.
Повний текст джерелаAl, Hakmani Rahab. "Bayesian Estimation of Mixture IRT Models using NUTS." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1641.
Повний текст джерелаLu, Pingbo. "Calibrated Bayes factors for model selection and model averaging." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343396705.
Повний текст джерелаDeng, 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.
Повний текст джерелаTitle 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.
Повний текст джерелаFu, Shuting. "Bayesian Logistic Regression Model with Integrated Multivariate Normal Approximation for Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/451.
Повний текст джерелаКниги з теми "Markov chain Monte Carlo samplers"
Liang, F. Advanced Markov chain Monte Carlo methods: Learning from past samples. Hoboken, NJ: Wiley, 2010.
Знайти повний текст джерелаHandbook for Markov chain Monte Carlo. Boca Raton: Taylor & Francis, 2011.
Знайти повний текст джерелаLiang, Faming, Chuanhai Liu, and Raymond J. Carroll. Advanced Markov Chain Monte Carlo Methods. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470669723.
Повний текст джерелаR, Gilks W., Richardson S, and Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. Boca Raton, Fla: Chapman & Hall, 1998.
Знайти повний текст джерелаR, Gilks W., Richardson S, and Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. London: Chapman & Hall, 1996.
Знайти повний текст джерелаS, Kendall W., Liang F. 1970-, and Wang J. S. 1960-, eds. Markov chain Monte Carlo: Innovations and applications. Singapore: World Scientific, 2005.
Знайти повний текст джерелаJoseph, 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.
Повний текст джерелаGamerman, Dani. Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. London: Chapman & Hall, 1997.
Знайти повний текст джерелаFreitas, Lopes Hedibert, ed. Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. 2nd ed. Boca Raton: Taylor & Francis, 2006.
Знайти повний текст джерелаMarkov chain Monte Carlo: Stochastic simulation for Bayesian inference. London: Chapman & Hall, 1997.
Знайти повний текст джерелаЧастини книг з теми "Markov chain Monte Carlo samplers"
Keith, Jonathan M., and Christian M. Davey. "Bayesian Approaches to the Design of Markov Chain Monte Carlo Samplers." In 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.
Повний текст джерелаAoki, Satoshi, Hisayuki Hara, and Akimichi Takemura. "Markov Chain Monte Carlo Methods over Discrete Sample Space." In 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.
Повний текст джерелаTanner, Martin A. "Markov Chain Monte Carlo: The Gibbs Sampler and the Metropolis Algorithm." In 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.
Повний текст джерелаKitchen, Nathan, and Andreas Kuehlmann. "A Markov Chain Monte Carlo Sampler for Mixed Boolean/Integer Constraints." In Computer Aided Verification, 446–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02658-4_34.
Повний текст джерелаTanner, Martin A. "Markov Chain Monte Carlo: The Gibbs Sampler and the Metropolis Algorithm." In Tools for Statistical Inference, 102–46. New York, NY: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4684-0192-9_6.
Повний текст джерелаYu, Thomas, Marco Pizzolato, Gabriel Girard, Jonathan Rafael-Patino, Erick Jorge Canales-Rodríguez, and Jean-Philippe Thiran. "Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler." In Lecture Notes in Computer Science, 818–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20351-1_64.
Повний текст джерелаSorensen, Daniel, and Daniel Gianola. "Markov Chain Monte Carlo." In 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.
Повний текст джерелаWedel, Michel, and Peter Lenk. "Markov Chain Monte Carlo." In 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.
Повний текст джерелаJoseph, Anosh. "Markov Chain Monte Carlo." In SpringerBriefs in Physics, 37–42. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46044-0_4.
Повний текст джерелаFürnkranz, Johannes, Philip K. Chan, Susan Craw, Claude Sammut, William Uther, Adwait Ratnaparkhi, Xin Jin, et al. "Markov Chain Monte Carlo." In Encyclopedia of Machine Learning, 639–42. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_511.
Повний текст джерелаТези доповідей конференцій з теми "Markov chain Monte Carlo samplers"
Vaiciulyte, Ingrida. "Adaptive Monte-Carlo Markov chain for multivariate statistical estimation." In 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.
Повний текст джерелаBURKETT, K. M., B. McNENEY, and J. GRAHAM. "A MARKOV CHAIN MONTE CARLO SAMPLER FOR GENE GENEALOGIES CONDITIONAL ON HAPLOTYPE DATA." In Proceedings of Statistics 2011 Canada/IMST 2011-FIM XX. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814417983_0003.
Повний текст джерелаGuzman, Rel. "Monte Carlo Methods on High Dimensional Data." In LatinX in AI at Neural Information Processing Systems Conference 2018. Journal of LatinX in AI Research, 2018. http://dx.doi.org/10.52591/lxai2018120314.
Повний текст джерелаPutze, A., L. Derome, F. Donato, and D. Maurin. "A Markov Chain Monte Carlo technique to sample transport and source parameters of Galactic cosmic rays." In Proceedings of the 12th ICATPP Conference. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814329033_0056.
Повний текст джерелаDavis, Gary A. "Sample-Based Estimation of Vehicle Speeds from Yaw Marks: Bayesian Implementation Using Markov Chain Monte Carlo Simulation." In SAE 2014 World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2014. http://dx.doi.org/10.4271/2014-01-0467.
Повний текст джерелаDe Sa, Christopher, Kunle Olukotun, and Christopher Ré. "Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling." In 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.
Повний текст джерелаLin, Pin-Yi, and Kuei-Yuan Chan. "Optimal Sample Augmentation and Resource Allocation for Design With Inadequate Uncertainty Data." In 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.
Повний текст джерелаAn, Dawn, and Joo-Ho Choi. "Improved MCMC Method for Parameter Estimation Based on Marginal Probability Density Function." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48784.
Повний текст джерелаWu, Y. T., A. P. Ku, and C. M. Serratella. "A Robust and Efficient Computational Method for Fatigue Reliability Update Using Inspected Data." In ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/omae2009-80034.
Повний текст джерелаBérešová, Simona. "Numerical realization of the Bayesian inversion accelerated using surrogate models." In Programs and Algorithms of Numerical Mathematics 21. Institute of Mathematics, Czech Academy of Sciences, 2023. http://dx.doi.org/10.21136/panm.2022.03.
Повний текст джерелаЗвіти організацій з теми "Markov chain Monte Carlo samplers"
Safta, Cosmin, Mohammad Khalil, and Habib N. Najm. Transitional Markov Chain Monte Carlo Sampler in UQTk. Office of Scientific and Technical Information (OSTI), March 2020. http://dx.doi.org/10.2172/1606084.
Повний текст джерелаGelfand, Alan E., and Sujit K. Sahu. On Markov Chain Monte Carlo Acceleration. Fort Belvoir, VA: Defense Technical Information Center, April 1994. http://dx.doi.org/10.21236/ada279393.
Повний текст джерелаWarnes, Gregory R. HYDRA: A Java Library for Markov Chain Monte Carlo. Fort Belvoir, VA: Defense Technical Information Center, March 2002. http://dx.doi.org/10.21236/ada459649.
Повний текст джерелаBates, Cameron Russell, and Edward Allen Mckigney. Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1417145.
Повний текст джерелаBaltz, E. Markov Chain Monte Carlo Exploration of Minimal Supergravity with Implications for Dark Matter. Office of Scientific and Technical Information (OSTI), July 2004. http://dx.doi.org/10.2172/827306.
Повний текст джерелаSethuraman, Jayaram. Easily Verifiable Conditions for the Convergence of the Markov Chain Monte Carlo Method. Fort Belvoir, VA: Defense Technical Information Center, December 1995. http://dx.doi.org/10.21236/ada308874.
Повний текст джерелаDoss, Hani. Studies in Reliability Theory and Survival Analysis and in Markov Chain Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada367895.
Повний текст джерелаDoss, Hani. Statistical Inference for Coherent Systems from Partial Information and Markov Chain Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, January 1996. http://dx.doi.org/10.21236/ada305676.
Повний текст джерелаDoss, Hani. Studies in Reliability Theory and Survival Analysis and in Markov Chain Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada379998.
Повний текст джерелаKnopp, Jeremy S., and 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, August 2012. http://dx.doi.org/10.21236/ada565876.
Повний текст джерела