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Relevant bibliographies by topics / Markov chain Monte Carlo

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Academic literature on the topic 'Markov chain Monte Carlo'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Markov chain Monte Carlo"

1

Adler, Jost, and Shahrzad Kurbiel. "Markov Chain Monte Carlo." WiSt - Wirtschaftswissenschaftliches Studium 44, no. 5 (2015): 238–45. http://dx.doi.org/10.15358/0340-1650-2015-5-238.

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Kuo, Lynn. "Markov Chain Monte Carlo." Technometrics 42, no. 2 (2000): 216. http://dx.doi.org/10.1080/00401706.2000.10486017.

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Chakraborty, Arnab. "Markov chain Monte Carlo." Resonance 7, no. 3 (2002): 25–34. http://dx.doi.org/10.1007/bf02896305.

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Chakraborty, Arnab. "Markov Chain Monte Carlo." Resonance 7, no. 5 (2002): 66–75. http://dx.doi.org/10.1007/bf02836738.

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Borkar, Vivek S. "Markov Chain Monte Carlo (MCMC)." Resonance 27, no. 7 (2022): 1107–15. http://dx.doi.org/10.1007/s12045-022-1407-1.

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Henry, Ronnie. "Etymologia: Markov Chain Monte Carlo." Emerging Infectious Diseases 25, no. 12 (2019): 2298. http://dx.doi.org/10.3201/eid2512.et2512.

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Sargent, Daniel J., James S. Hodges, and Bradley P. Carlin. "Structured Markov Chain Monte Carlo." Journal of Computational and Graphical Statistics 9, no. 2 (2000): 217. http://dx.doi.org/10.2307/1390651.

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Sargent, Daniel J., James S. Hodges, and Bradley P. Carlin. "Structured Markov Chain Monte Carlo." Journal of Computational and Graphical Statistics 9, no. 2 (2000): 217–34. http://dx.doi.org/10.1080/10618600.2000.10474877.

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Geyer, Charles J. "Practical Markov Chain Monte Carlo." Statistical Science 7, no. 4 (1992): 473–83. http://dx.doi.org/10.1214/ss/1177011137.

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Dodwell, T. J., C. Ketelsen, R. Scheichl, and A. L. Teckentrup. "Multilevel Markov Chain Monte Carlo." SIAM Review 61, no. 3 (2019): 509–45. http://dx.doi.org/10.1137/19m126966x.

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Dissertations / Theses on the topic "Markov chain Monte Carlo"

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Bakra, Eleni. "Aspects of population Markov chain Monte Carlo and reversible jump Markov chain Monte Carlo." Thesis, University of Glasgow, 2009. http://theses.gla.ac.uk/1247/.

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Holenstein, Roman. "Particle Markov chain Monte Carlo." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/7319.

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Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods have emerged as the two main tools to sample from high-dimensional probability distributions. Although asymptotic convergence of MCMC algorithms is ensured under weak assumptions, the performance of these latters is unreliable when the proposal distributions used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. In this thesis we propose a new Monte Carlo framework in which we build efficient high-dimensional proposal distributions using SMC methods. This allows us to

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Byrd, Jonathan Michael Robert. "Parallel Markov Chain Monte Carlo." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/3634/.

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The increasing availability of multi-core and multi-processor architectures provides new opportunities for improving the performance of many computer simulations. Markov Chain Monte Carlo (MCMC) simulations are widely used for approximate counting problems, Bayesian inference and as a means for estimating very highdimensional integrals. As such MCMC has found a wide variety of applications in fields including computational biology and physics,financial econometrics, machine learning and image processing. This thesis presents a number of new method for reducing the runtime of Markov Chain Monte

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Zhang, Yichuan. "Scalable geometric Markov chain Monte Carlo." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20978.

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Markov chain Monte Carlo (MCMC) is one of the most popular statistical inference methods in machine learning. Recent work shows that a significant improvement of the statistical efficiency of MCMC on complex distributions can be achieved by exploiting geometric properties of the target distribution. This is known as geometric MCMC. However, many such methods, like Riemannian manifold Hamiltonian Monte Carlo (RMHMC), are computationally challenging to scale up to high dimensional distributions. The primary goal of this thesis is to develop novel geometric MCMC methods applicable to large-scale

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Fang, Youhan. "Efficient Markov Chain Monte Carlo Methods." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10809188.

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<p> Generating random samples from a prescribed distribution is one of the most important and challenging problems in machine learning, Bayesian statistics, and the simulation of materials. Markov Chain Monte Carlo (MCMC) methods are usually the required tool for this task, if the desired distribution is known only up to a multiplicative constant. Samples produced by an MCMC method are real values in <i>N</i>-dimensional space, called the configuration space. The distribution of such samples converges to the target distribution in the limit. However, existing MCMC methods still face many chall

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Neuhoff, Daniel. "Reversible Jump Markov Chain Monte Carlo." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17461.

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Die vier in der vorliegenden Dissertation enthaltenen Studien beschäftigen sich vorwiegend mit dem dynamischen Verhalten makroökonomischer Zeitreihen. Diese Dynamiken werden sowohl im Kontext eines einfachen DSGE Modells, als auch aus der Sichtweise reiner Zeitreihenmodelle untersucht.<br>The four studies of this thesis are concerned predominantly with the dynamics of macroeconomic time series, both in the context of a simple DSGE model, as well as from a pure time series modeling perspective.

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Murray, Iain Andrew. "Advances in Markov chain Monte Carlo methods." Thesis, University College London (University of London), 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487199.

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Probability distributions over many variables occur frequently in Bayesian inference, statistical physics and simulation studies. Samples from distributions give insight into their typical behavior and can allow approximation of any quantity of interest, such as expectations or normalizing constants. Markov chain Monte Carlo (MCMC), introduced by Metropolis et al. (1953), allows r sampling from distributions with intractable normalization, and remains one of most important tools for approximate computation with probability distributions. I While not needed by MCMC, normalizers are key quantiti

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Han, Xiao-liang. "Markov Chain Monte Carlo and sampling efficiency." Thesis, University of Bristol, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333974.

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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.

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Brooks, Stephen Peter. "Convergence diagnostics for Markov Chain Monte Carlo." Thesis, University of Cambridge, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.363913.

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Books on the topic "Markov chain Monte Carlo"

1

R, Gilks W., Richardson S, and Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. Chapman & Hall, 1996.

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R, Gilks W., Richardson S, and Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. Chapman & Hall, 1998.

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Liang, Faming, Chuanhai Liu, and Raymond J. Carroll. Advanced Markov Chain Monte Carlo Methods. John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470669723.

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4

S, Kendall W., Liang F. 1970-, and Wang J. S. 1960-, eds. Markov chain Monte Carlo: Innovations and applications. World Scientific, 2005.

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5

Liang, F. Advanced Markov chain Monte Carlo methods: Learning from past samples. Wiley, 2010.

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Joseph, Anosh. Markov Chain Monte Carlo Methods in Quantum Field Theories. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46044-0.

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7

Gamerman, Dani. Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. Chapman & Hall, 1997.

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Freitas, Lopes Hedibert, ed. Markov chain Monte Carlo: Stochastic simulation for Bayesian inference. 2nd ed. Taylor & Francis, 2006.

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9

Roberts, Gareth O. Markov chain Monte Carlo: Some practical implications of theoretical results. University of Toronto, 1997.

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10

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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Book chapters on the topic "Markov chain Monte Carlo"

1

Sorensen, Daniel, and Daniel Gianola. "Markov Chain Monte Carlo." In Statistics for Biology and Health. Springer New York, 2002. http://dx.doi.org/10.1007/0-387-22764-4_11.

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2

Wedel, Michel, and Peter Lenk. "Markov Chain Monte Carlo." In Encyclopedia of Operations Research and Management Science. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1164.

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Joseph, Anosh. "Markov Chain Monte Carlo." In SpringerBriefs in Physics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46044-0_4.

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Fürnkranz, Johannes, Philip K. Chan, Susan Craw, et al. "Markov Chain Monte Carlo." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_511.

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Wang, Lin. "Markov Chain Monte Carlo." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_427.

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Chopin, Nicolas, and Omiros Papaspiliopoulos. "Markov Chain Monte Carlo." In Springer Series in Statistics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47845-2_15.

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7

Lange, Kenneth. "Markov Chain Monte Carlo." In Numerical Analysis for Statisticians. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5945-4_26.

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8

Lawler, Gregory, and Lester Coyle. "Markov chain Monte Carlo." In The Student Mathematical Library. American Mathematical Society, 1999. http://dx.doi.org/10.1090/stml/002/08.

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9

Hooten, Mevin B., and Trevor J. Hefley. "Markov Chain Monte Carlo." In Bringing Bayesian Models to Life. CRC Press, 2019. http://dx.doi.org/10.1201/9780429243653-4.

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Shonkwiler, Ronald W., and Franklin Mendivil. "Markov Chain Monte Carlo." In Undergraduate Texts in Mathematics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-87837-9_3.

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Conference papers on the topic "Markov chain Monte Carlo"

1

Derakhshanian, Nasim, Peter Risse, T. Jezo, K. Kovarik, and A. Kusina. "Estimating nPDF Uncertainties via Markov Chain Monte Carlo Methods." In 31st International Workshop on Deep Inelastic Scattering. Sissa Medialab, 2024. https://doi.org/10.22323/1.469.0058.

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2

Lopez, Enzo, Karim Dahia, Nicolas Merlinge, Benedicte Winter-Bonnet, Alain Maschiella, and Christian Musso. "Sequential Markov Chain Monte Carlo methods on Matrix Lie Groups." In 2024 27th International Conference on Information Fusion (FUSION). IEEE, 2024. http://dx.doi.org/10.23919/fusion59988.2024.10706305.

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Zhan, Xianghao, Nafiul Rashid, Ebrahim Nemati, Mohsin Y. Ahmed, and Jilong Kuang. "Earbuds Orientation Alignment Based on Markov Chain Monte Carlo Sampling." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888055.

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4

Feng, Weiming, Heng Guo, Chunyang Wang, Jiaheng Wang, and Yitong Yin. "Towards derandomising Markov chain Monte Carlo." In 2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2023. http://dx.doi.org/10.1109/focs57990.2023.00120.

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5

Spall, J. C. "Estimation via Markov chain Monte Carlo." In Proceedings of 2002 American Control Conference. IEEE, 2002. http://dx.doi.org/10.1109/acc.2002.1025170.

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Dolce, Michael. "Fitting NOvA cross-section parameters with Markov Chain Monte Carlo." In Fitting NOvA cross-section parameters with Markov Chain Monte Carlo. US DOE, 2021. http://dx.doi.org/10.2172/1827879.

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Suzuki, Yuya, and Thorbjörn Gudmundsson. "Markov Chain Monte Carlo for Risk Measures." In 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SCITEPRESS - Science and Technology Publications, 2014. http://dx.doi.org/10.5220/0005035303300338.

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Sundaram, V., and K. P. N. Murthy. "MIMO detection employing Markov Chain Monte Carlo." In TENCON 2008 - 2008 IEEE Region 10 Conference (TENCON). IEEE, 2008. http://dx.doi.org/10.1109/tencon.2008.4766505.

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Wang, Danling, Qin Zhang, and John Morris. "Distributed Markov Chain Monte Carlo Particle Filtering." In 2009 2nd International Conference on Computer Science and its Applications (CSA). IEEE, 2009. http://dx.doi.org/10.1109/csa.2009.5404264.

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Jalali, Shirin, and Tsachy Weissman. "Rate-distortion via Markov chain Monte Carlo." In 2008 IEEE International Symposium on Information Theory - ISIT. IEEE, 2008. http://dx.doi.org/10.1109/isit.2008.4595107.

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Reports on the topic "Markov chain Monte Carlo"

1

Gelfand, Alan E., and Sujit K. Sahu. On Markov Chain Monte Carlo Acceleration. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada279393.

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Safta, Cosmin, Mohammad Khalil, and Habib N. Najm. Transitional Markov Chain Monte Carlo Sampler in UQTk. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1606084.

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3

Warnes, Gregory R. HYDRA: A Java Library for Markov Chain Monte Carlo. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada459649.

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Reddy, S., and A. Crisp. Deep Neural Network Informed Markov Chain Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2283285.

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Bates, Cameron Russell, and Edward Allen Mckigney. Metis: A Pure Metropolis Markov Chain Monte Carlo Bayesian Inference Library. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1417145.

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Small, Matthew. Determining the Mass Function of Planetesimals Using Markov Chain Monte Carlo Simulations. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-524.

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Baltz, E. Markov Chain Monte Carlo Exploration of Minimal Supergravity with Implications for Dark Matter. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/827306.

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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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Doss, Hani. Studies in Reliability Theory and Survival Analysis and in Markov Chain Monte Carlo Methods. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada367895.

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Doss, Hani. Statistical Inference for Coherent Systems from Partial Information and Markov Chain Monte Carlo Methods. Defense Technical Information Center, 1996. http://dx.doi.org/10.21236/ada305676.

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