Academic literature on the topic 'Markov chain Monte Carlo methods'
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Journal articles on the topic "Markov chain Monte Carlo methods"
Athreya, K. B., Mohan Delampady, and T. Krishnan. "Markov Chain Monte Carlo methods." Resonance 8, no. 12 (December 2003): 18–32. http://dx.doi.org/10.1007/bf02839048.
Full textAthreya, K. B., Mohan Delampady, and T. Krishnan. "Markov chain Monte Carlo methods." Resonance 8, no. 10 (October 2003): 8–19. http://dx.doi.org/10.1007/bf02840702.
Full textAthreya, K. B., Mohan Delampady, and T. Krishnan. "Markov chain Monte Carlo methods." Resonance 8, no. 7 (July 2003): 63–75. http://dx.doi.org/10.1007/bf02834404.
Full textAthreya, K. B., Mohan Delampady, and T. Krishnan. "Markov Chain Monte Carlo methods." Resonance 8, no. 4 (April 2003): 17–26. http://dx.doi.org/10.1007/bf02883528.
Full textAndrieu, Christophe, Arnaud Doucet, and Roman Holenstein. "Particle Markov chain Monte Carlo methods." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72, no. 3 (June 2010): 269–342. http://dx.doi.org/10.1111/j.1467-9868.2009.00736.x.
Full textGelman, Andrew, and Donald B. Rubin. "Markov chain Monte Carlo methods in biostatistics." Statistical Methods in Medical Research 5, no. 4 (December 1996): 339–55. http://dx.doi.org/10.1177/096228029600500402.
Full textBrockwell, Anthony, Pierre Del Moral, and Arnaud Doucet. "Sequentially interacting Markov chain Monte Carlo methods." Annals of Statistics 38, no. 6 (December 2010): 3387–411. http://dx.doi.org/10.1214/09-aos747.
Full textJones, Galin L., and Qian Qin. "Markov Chain Monte Carlo in Practice." Annual Review of Statistics and Its Application 9, no. 1 (March 7, 2022): 557–78. http://dx.doi.org/10.1146/annurev-statistics-040220-090158.
Full textJones, Galin L., and Qian Qin. "Markov Chain Monte Carlo in Practice." Annual Review of Statistics and Its Application 9, no. 1 (March 7, 2022): 557–78. http://dx.doi.org/10.1146/annurev-statistics-040220-090158.
Full textMontanaro, Ashley. "Quantum speedup of Monte Carlo methods." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, no. 2181 (September 2015): 20150301. http://dx.doi.org/10.1098/rspa.2015.0301.
Full textDissertations / Theses on the topic "Markov chain Monte Carlo methods"
Fang, Youhan. "Efficient Markov Chain Monte Carlo Methods." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10809188.
Full textGenerating 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 N-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 challenges that are not well resolved. Difficulties for sampling by using MCMC methods include, but not exclusively, dealing with high dimensional and multimodal problems, high computation cost due to extremely large datasets in Bayesian machine learning models, and lack of reliable indicators for detecting convergence and measuring the accuracy of sampling. This dissertation focuses on new theory and methodology for efficient MCMC methods that aim to overcome the aforementioned difficulties.
One contribution of this dissertation is generalizations of hybrid Monte Carlo (HMC). An HMC method combines a discretized dynamical system in an extended space, called the state space, and an acceptance test based on the Metropolis criterion. The discretized dynamical system used in HMC is volume preserving—meaning that in the state space, the absolute Jacobian of a map from one point on the trajectory to another is 1. Volume preservation is, however, not necessary for the general purpose of sampling. A general theory allowing the use of non-volume preserving dynamics for proposing MCMC moves is proposed. Examples including isokinetic dynamics and variable mass Hamiltonian dynamics with an explicit integrator, are all designed with fewer restrictions based on the general theory. Experiments show improvement in efficiency for sampling high dimensional multimodal problems. A second contribution is stochastic gradient samplers with reduced bias. An in-depth analysis of the noise introduced by the stochastic gradient is provided. Two methods to reduce the bias in the distribution of samples are proposed. One is to correct the dynamics by using an estimated noise based on subsampled data, and the other is to introduce additional variables and corresponding dynamics to adaptively reduce the bias. Extensive experiments show that both methods outperform existing methods. A third contribution is quasi-reliable estimates of effective sample size. Proposed is a more reliable indicator—the longest integrated autocorrelation time over all functions in the state space—for detecting the convergence and measuring the accuracy of MCMC methods. The superiority of the new indicator is supported by experiments on both synthetic and real problems.
Minor contributions include a general framework of changing variables, and a numerical integrator for the Hamiltonian dynamics with fourth order accuracy. The idea of changing variables is to transform the potential energy function as a function of the original variable to a function of the new variable, such that undesired properties can be removed. Two examples are provided and preliminary experimental results are obtained for supporting this idea. The fourth order integrator is constructed by combining the idea of the simplified Takahashi-Imada method and a two-stage Hessian-based integrator. The proposed method, called two-stage simplified Takahashi-Imada method, shows outstanding performance over existing methods in high-dimensional sampling problems.
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.
Full textGraham, Matthew McKenzie. "Auxiliary variable Markov chain Monte Carlo methods." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28962.
Full textXu, Jason Qian. "Markov Chain Monte Carlo and Non-Reversible Methods." Thesis, The University of Arizona, 2012. http://hdl.handle.net/10150/244823.
Full textZhang, Yichuan. "Scalable geometric Markov chain Monte Carlo." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20978.
Full textPereira, Fernanda Chaves. "Bayesian Markov chain Monte Carlo methods in general insurance." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342720.
Full textCheal, 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.
Full textDurmus, Alain. "High dimensional Markov chain Monte Carlo methods : theory, methods and applications." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLT001/document.
Full textThe subject of this thesis is the analysis of Markov Chain Monte Carlo (MCMC) methods and the development of new methodologies to sample from a high dimensional distribution. Our work is divided into three main topics. The first problem addressed in this manuscript is the convergence of Markov chains in Wasserstein distance. Geometric and sub-geometric convergence with explicit constants, are derived under appropriate conditions. These results are then applied to thestudy of MCMC algorithms. The first analyzed algorithm is an alternative scheme to the Metropolis Adjusted Langevin algorithm for which explicit geometric convergence bounds are established. The second method is the pre-Conditioned Crank-Nicolson algorithm. It is shown that under mild assumption, the Markov chain associated with thisalgorithm is sub-geometrically ergodic in an appropriated Wasserstein distance. The second topic of this thesis is the study of the Unadjusted Langevin algorithm (ULA). We are first interested in explicit convergence bounds in total variation under different kinds of assumption on the potential associated with the target distribution. In particular, we pay attention to the dependence of the algorithm on the dimension of the state space. The case of fixed step sizes as well as the case of nonincreasing sequences of step sizes are dealt with. When the target density is strongly log-concave, explicit bounds in Wasserstein distance are established. These results are then used to derived new bounds in the total variation distance which improve the one previously derived under weaker conditions on the target density.The last part tackles new optimal scaling results for Metropolis-Hastings type algorithms. First, we extend the pioneer result on the optimal scaling of the random walk Metropolis algorithm to target densities which are differentiable in Lp mean for p ≥ 2. Then, we derive new Metropolis-Hastings type algorithms which have a better optimal scaling compared the MALA algorithm. Finally, the stability and the convergence in total variation of these new algorithms are studied
Wu, Miaodan. "Markov chain Monte Carlo methods applied to Bayesian data analysis." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.625087.
Full textPaul, Rajib. "Theoretical And Algorithmic Developments In Markov Chain Monte Carlo." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218184168.
Full textBooks on the topic "Markov chain Monte Carlo methods"
Liang, 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.
Full textHandbook for Markov chain Monte Carlo. Boca Raton: Taylor & Francis, 2011.
Find full textR, Gilks W., Richardson S, and Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. Boca Raton, Fla: Chapman & Hall, 1998.
Find full textR, Gilks W., Richardson S, and Spiegelhalter D. J, eds. Markov chain Monte Carlo in practice. London: Chapman & Hall, 1996.
Find full textLiang, F. Advanced Markov chain Monte Carlo methods: Learning from past samples. Hoboken, NJ: Wiley, 2010.
Find full textJoseph, 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.
Full textS, Kendall W., Liang F. 1970-, and Wang J. S. 1960-, eds. Markov chain Monte Carlo: Innovations and applications. Singapore: World Scientific, 2005.
Find full text1946-, Winkler Gerhard, ed. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Berlin: Springer, 2003.
Find full textGerhard, Winkler. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Berlin: Springer, 2003.
Find full textWinkler, Gerhard. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55760-6.
Full textBook chapters on the topic "Markov chain Monte Carlo methods"
Barbu, Adrian, and Song-Chun Zhu. "Markov Chain Monte Carlo: The Basics." In Monte Carlo Methods, 49–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5_3.
Full textBarbu, Adrian, and Song-Chun Zhu. "Data Driven Markov Chain Monte Carlo." In Monte Carlo Methods, 211–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5_8.
Full textLi, Hang. "Markov Chain Monte Carlo Method." In Machine Learning Methods, 401–37. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_19.
Full textÓ Ruanaidh, Joseph J. K., and William J. Fitzgerald. "Markov Chain Monte Carlo Methods." In Numerical Bayesian Methods Applied to Signal Processing, 69–95. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0717-7_4.
Full textRobert, Christian P., and Sylvia Richardson. "Markov Chain Monte Carlo Methods." In Discretization and MCMC Convergence Assessment, 1–25. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1716-9_1.
Full textLange, Kenneth. "Markov Chain Monte Carlo Methods." In Mathematical and Statistical Methods for Genetic Analysis, 142–63. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2739-5_9.
Full textHörmann, Wolfgang, Josef Leydold, and Gerhard Derflinger. "Markov Chain Monte Carlo Methods." In Automatic Nonuniform Random Variate Generation, 363–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05946-3_14.
Full textAlbert, Jim. "Markov Chain Monte Carlo Methods." In Bayesian Computation with R, 117–52. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92298-0_6.
Full textNeifer, Thomas. "Markov Chain Monte Carlo Methods." In Springer Texts in Business and Economics, 167–83. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47206-0_9.
Full textChib, Siddhartha. "Markov Chain Monte Carlo Methods." In The New Palgrave Dictionary of Economics, 1–11. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2042-1.
Full textConference papers on the topic "Markov chain Monte Carlo methods"
Runnalls, A. "Monte Carlo Markov chain methods for tracking." In IEE Colloquium on `Algorithms for Target Tracking'. IEE, 1995. http://dx.doi.org/10.1049/ic:19950668.
Full textWadsley, Andrew W. "Markov Chain Monte Carlo Methods for Reserves Estimation." In International Petroleum Technology Conference. International Petroleum Technology Conference, 2005. http://dx.doi.org/10.2523/iptc-10065-ms.
Full textSomersalo, Erkki, Jari P. Kaipio, Marko J. Vauhkonen, D. Baroudi, and S. Jaervenpaeae. "Impedance imaging and Markov chain Monte Carlo methods." In Optical Science, Engineering and Instrumentation '97, edited by Randall L. Barbour, Mark J. Carvlin, and Michael A. Fiddy. SPIE, 1997. http://dx.doi.org/10.1117/12.279723.
Full textWadsley, Andrew W. "Markov Chain Monte Carlo Methods for Reserves Estimation." In International Petroleum Technology Conference. International Petroleum Technology Conference, 2005. http://dx.doi.org/10.2523/10065-ms.
Full textGerencser, L., S. D. Hill, Z. Vago, and Z. Vincze. "Discrete optimization, SPSA and Markov chain Monte Carlo methods." In Proceedings of the 2004 American Control Conference. IEEE, 2004. http://dx.doi.org/10.23919/acc.2004.1384507.
Full textde Figueiredo, L. Passos, D. Grana, M. Roisenberg, and B. Rodrigues. "Markov Chain Monte Carlo Methods for High-dimensional Mixture Distributions." In Petroleum Geostatistics 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902273.
Full textNabarrete, Airton, José Antonio Hernandes, and Rafael Beal Macedo. "BAYESIAN DYNAMIC MODEL UPDATING USING MARKOV CHAIN MONTE CARLO METHODS." In 24th ABCM International Congress of Mechanical Engineering. ABCM, 2017. http://dx.doi.org/10.26678/abcm.cobem2017.cob17-1646.
Full textRunnalls, A. "Low-observable maritime tracking using Monte Carlo Markov chain methods." In IEE Colloquium on Target Tracking and Data Fusion. IEE, 1996. http://dx.doi.org/10.1049/ic:19961354.
Full textvan Lieshout, M. N. M. "Markov chain Monte Carlo methods for clustering of image features." In Fifth International Conference on Image Processing and its Applications. IEE, 1995. http://dx.doi.org/10.1049/cp:19950657.
Full textAkoum, S., R. Peng, R. R. Chen, and B. Farhang-Boroujeny. "Markov Chain Monte Carlo Detection Methods for High SNR Regimes." In ICC 2009 - 2009 IEEE International Conference on Communications. IEEE, 2009. http://dx.doi.org/10.1109/icc.2009.5199166.
Full textReports on the topic "Markov chain Monte Carlo methods"
Reddy, S., and A. Crisp. Deep Neural Network Informed Markov Chain Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), November 2023. http://dx.doi.org/10.2172/2283285.
Full textDoss, 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.
Full textDoss, 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.
Full textDoss, 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.
Full textSethuraman, 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.
Full textGlaser, 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), March 2004. http://dx.doi.org/10.2172/15009813.
Full textKnopp, 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.
Full textWarnes, Gregory R. The Normal Kernel Coupler: An Adaptive Markov Chain Monte Carlo Method for Efficiently Sampling From Multi-Modal Distributions. Fort Belvoir, VA: Defense Technical Information Center, March 2001. http://dx.doi.org/10.21236/ada459460.
Full textZang, Emma. Bayesian Statistics for Social and Health Scientists in R and Python. Instats Inc., 2023. http://dx.doi.org/10.61700/obtt1o65iw3ui469.
Full textZang, Emma. Bayesian Statistics for Social and Health Scientists in R and Python + 2 Free Seminars. Instats Inc., 2022. http://dx.doi.org/10.61700/bgfpomu3wdhe5469.
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