Literatura académica sobre el tema "Markov chain Monte Carlo methods"
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Artículos de revistas sobre el tema "Markov chain Monte Carlo methods"
Athreya, K. B., Mohan Delampady y T. Krishnan. "Markov Chain Monte Carlo methods". Resonance 8, n.º 12 (diciembre de 2003): 18–32. http://dx.doi.org/10.1007/bf02839048.
Texto completoAthreya, K. B., Mohan Delampady y T. Krishnan. "Markov chain Monte Carlo methods". Resonance 8, n.º 10 (octubre de 2003): 8–19. http://dx.doi.org/10.1007/bf02840702.
Texto completoAthreya, K. B., Mohan Delampady y T. Krishnan. "Markov chain Monte Carlo methods". Resonance 8, n.º 7 (julio de 2003): 63–75. http://dx.doi.org/10.1007/bf02834404.
Texto completoAthreya, K. B., Mohan Delampady y T. Krishnan. "Markov Chain Monte Carlo methods". Resonance 8, n.º 4 (abril de 2003): 17–26. http://dx.doi.org/10.1007/bf02883528.
Texto completoAndrieu, Christophe, Arnaud Doucet y Roman Holenstein. "Particle Markov chain Monte Carlo methods". Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72, n.º 3 (junio de 2010): 269–342. http://dx.doi.org/10.1111/j.1467-9868.2009.00736.x.
Texto completoGelman, Andrew y Donald B. Rubin. "Markov chain Monte Carlo methods in biostatistics". Statistical Methods in Medical Research 5, n.º 4 (diciembre de 1996): 339–55. http://dx.doi.org/10.1177/096228029600500402.
Texto completoBrockwell, Anthony, Pierre Del Moral y Arnaud Doucet. "Sequentially interacting Markov chain Monte Carlo methods". Annals of Statistics 38, n.º 6 (diciembre de 2010): 3387–411. http://dx.doi.org/10.1214/09-aos747.
Texto completoJones, Galin L. y Qian Qin. "Markov Chain Monte Carlo in Practice". Annual Review of Statistics and Its Application 9, n.º 1 (7 de marzo de 2022): 557–78. http://dx.doi.org/10.1146/annurev-statistics-040220-090158.
Texto completoJones, Galin L. y Qian Qin. "Markov Chain Monte Carlo in Practice". Annual Review of Statistics and Its Application 9, n.º 1 (7 de marzo de 2022): 557–78. http://dx.doi.org/10.1146/annurev-statistics-040220-090158.
Texto completoMontanaro, Ashley. "Quantum speedup of Monte Carlo methods". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, n.º 2181 (septiembre de 2015): 20150301. http://dx.doi.org/10.1098/rspa.2015.0301.
Texto completoTesis sobre el tema "Markov chain Monte Carlo methods"
Fang, Youhan. "Efficient Markov Chain Monte Carlo Methods". Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10809188.
Texto completoGenerating 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.
Texto completoGraham, Matthew McKenzie. "Auxiliary variable Markov chain Monte Carlo methods". Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28962.
Texto completoXu, Jason Qian. "Markov Chain Monte Carlo and Non-Reversible Methods". Thesis, The University of Arizona, 2012. http://hdl.handle.net/10150/244823.
Texto completoZhang, Yichuan. "Scalable geometric Markov chain Monte Carlo". Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20978.
Texto completoPereira, 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.
Texto completoCheal, 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.
Texto completoDurmus, Alain. "High dimensional Markov chain Monte Carlo methods : theory, methods and applications". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLT001/document.
Texto completoThe 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.
Texto completoPaul, Rajib. "Theoretical And Algorithmic Developments In Markov Chain Monte Carlo". The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218184168.
Texto completoLibros sobre el tema "Markov chain Monte Carlo methods"
Liang, 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 completoHandbook for Markov chain Monte Carlo. Boca Raton: Taylor & Francis, 2011.
Buscar 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 completoLiang, F. Advanced Markov chain Monte Carlo methods: Learning from past samples. Hoboken, NJ: Wiley, 2010.
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 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 completo1946-, Winkler Gerhard, ed. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2a ed. Berlin: Springer, 2003.
Buscar texto completoGerhard, Winkler. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2a ed. Berlin: Springer, 2003.
Buscar texto completoWinkler, 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.
Texto completoCapítulos de libros sobre el tema "Markov chain Monte Carlo methods"
Barbu, Adrian y Song-Chun Zhu. "Markov Chain Monte Carlo: The Basics". En Monte Carlo Methods, 49–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5_3.
Texto completoBarbu, Adrian y Song-Chun Zhu. "Data Driven Markov Chain Monte Carlo". En Monte Carlo Methods, 211–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5_8.
Texto completoLi, Hang. "Markov Chain Monte Carlo Method". En Machine Learning Methods, 401–37. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_19.
Texto completoÓ Ruanaidh, Joseph J. K. y William J. Fitzgerald. "Markov Chain Monte Carlo Methods". En 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.
Texto completoRobert, Christian P. y Sylvia Richardson. "Markov Chain Monte Carlo Methods". En 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.
Texto completoLange, Kenneth. "Markov Chain Monte Carlo Methods". En 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.
Texto completoHörmann, Wolfgang, Josef Leydold y Gerhard Derflinger. "Markov Chain Monte Carlo Methods". En 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.
Texto completoAlbert, Jim. "Markov Chain Monte Carlo Methods". En 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.
Texto completoNeifer, Thomas. "Markov Chain Monte Carlo Methods". En 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.
Texto completoChib, Siddhartha. "Markov Chain Monte Carlo Methods". En 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.
Texto completoActas de conferencias sobre el tema "Markov chain Monte Carlo methods"
Runnalls, A. "Monte Carlo Markov chain methods for tracking". En IEE Colloquium on `Algorithms for Target Tracking'. IEE, 1995. http://dx.doi.org/10.1049/ic:19950668.
Texto completoWadsley, Andrew W. "Markov Chain Monte Carlo Methods for Reserves Estimation". En International Petroleum Technology Conference. International Petroleum Technology Conference, 2005. http://dx.doi.org/10.2523/iptc-10065-ms.
Texto completoSomersalo, Erkki, Jari P. Kaipio, Marko J. Vauhkonen, D. Baroudi y S. Jaervenpaeae. "Impedance imaging and Markov chain Monte Carlo methods". En Optical Science, Engineering and Instrumentation '97, editado por Randall L. Barbour, Mark J. Carvlin y Michael A. Fiddy. SPIE, 1997. http://dx.doi.org/10.1117/12.279723.
Texto completoWadsley, Andrew W. "Markov Chain Monte Carlo Methods for Reserves Estimation". En International Petroleum Technology Conference. International Petroleum Technology Conference, 2005. http://dx.doi.org/10.2523/10065-ms.
Texto completoGerencser, L., S. D. Hill, Z. Vago y Z. Vincze. "Discrete optimization, SPSA and Markov chain Monte Carlo methods". En Proceedings of the 2004 American Control Conference. IEEE, 2004. http://dx.doi.org/10.23919/acc.2004.1384507.
Texto completode Figueiredo, L. Passos, D. Grana, M. Roisenberg y B. Rodrigues. "Markov Chain Monte Carlo Methods for High-dimensional Mixture Distributions". En Petroleum Geostatistics 2019. European Association of Geoscientists & Engineers, 2019. http://dx.doi.org/10.3997/2214-4609.201902273.
Texto completoNabarrete, Airton, José Antonio Hernandes y Rafael Beal Macedo. "BAYESIAN DYNAMIC MODEL UPDATING USING MARKOV CHAIN MONTE CARLO METHODS". En 24th ABCM International Congress of Mechanical Engineering. ABCM, 2017. http://dx.doi.org/10.26678/abcm.cobem2017.cob17-1646.
Texto completoRunnalls, A. "Low-observable maritime tracking using Monte Carlo Markov chain methods". En IEE Colloquium on Target Tracking and Data Fusion. IEE, 1996. http://dx.doi.org/10.1049/ic:19961354.
Texto completovan Lieshout, M. N. M. "Markov chain Monte Carlo methods for clustering of image features". En Fifth International Conference on Image Processing and its Applications. IEE, 1995. http://dx.doi.org/10.1049/cp:19950657.
Texto completoAkoum, S., R. Peng, R. R. Chen y B. Farhang-Boroujeny. "Markov Chain Monte Carlo Detection Methods for High SNR Regimes". En ICC 2009 - 2009 IEEE International Conference on Communications. IEEE, 2009. http://dx.doi.org/10.1109/icc.2009.5199166.
Texto completoInformes sobre el tema "Markov chain Monte Carlo methods"
Reddy, S. y A. Crisp. Deep Neural Network Informed Markov Chain Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), noviembre de 2023. http://dx.doi.org/10.2172/2283285.
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 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 completoGlaser, 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), marzo de 2004. http://dx.doi.org/10.2172/15009813.
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.
Texto completoWarnes, 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, marzo de 2001. http://dx.doi.org/10.21236/ada459460.
Texto completoZang, Emma. Bayesian Statistics for Social and Health Scientists in R and Python. Instats Inc., 2023. http://dx.doi.org/10.61700/obtt1o65iw3ui469.
Texto completoZang, 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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