Relevant bibliographies by topics / Markov chain Monte Carlo methods

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Markov chain Monte Carlo methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 October 2024

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Journal articles on the topic "Markov chain Monte Carlo methods"

1

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.

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Athreya, 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.

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Athreya, 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.

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Athreya, 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.

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Andrieu, 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.

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Gelman, 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.

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Brockwell, 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.

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Montanaro, 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.

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Monte Carlo methods use random sampling to estimate numerical quantities which are hard to compute deterministically. One important example is the use in statistical physics of rapidly mixing Markov chains to approximately compute partition functions. In this work, we describe a quantum algorithm which can accelerate Monte Carlo methods in a very general setting. The algorithm estimates the expected output value of an arbitrary randomized or quantum subroutine with bounded variance, achieving a near-quadratic speedup over the best possible classical algorithm. Combining the algorithm with the
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Jones, 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.

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Markov chain Monte Carlo (MCMC) is an essential set of tools for estimating features of probability distributions commonly encountered in modern applications. For MCMC simulation to produce reliable outcomes, it needs to generate observations representative of the target distribution, and it must be long enough so that the errors of Monte Carlo estimates are small. We review methods for assessing the reliability of the simulation effort, with an emphasis on those most useful in practically relevant settings. Both strengths and weaknesses of these methods are discussed. The methods are illustra
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Jones, 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.

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Markov chain Monte Carlo (MCMC) is an essential set of tools for estimating features of probability distributions commonly encountered in modern applications. For MCMC simulation to produce reliable outcomes, it needs to generate observations representative of the target distribution, and it must be long enough so that the errors of Monte Carlo estimates are small. We review methods for assessing the reliability of the simulation effort, with an emphasis on those most useful in practically relevant settings. Both strengths and weaknesses of these methods are discussed. The methods are illustra
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Dissertations / Theses on the topic "Markov chain Monte Carlo methods"

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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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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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Graham, Matthew McKenzie. "Auxiliary variable Markov chain Monte Carlo methods." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28962.

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Markov chain Monte Carlo (MCMC) methods are a widely applicable class of algorithms for estimating integrals in statistical inference problems. A common approach in MCMC methods is to introduce additional auxiliary variables into the Markov chain state and perform transitions in the joint space of target and auxiliary variables. In this thesis we consider novel methods for using auxiliary variables within MCMC methods to allow approximate inference in otherwise intractable models and to improve sampling performance in models exhibiting challenging properties such as multimodality. We first con
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Xu, Jason Qian. "Markov Chain Monte Carlo and Non-Reversible Methods." Thesis, The University of Arizona, 2012. http://hdl.handle.net/10150/244823.

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The bulk of Markov chain Monte Carlo applications make use of reversible chains, relying on the Metropolis-Hastings algorithm or similar methods. While reversible chains have the advantage of being relatively easy to analyze, it has been shown that non-reversible chains may outperform them in various scenarios. Neal proposes an algorithm that transforms a general reversible chain into a non-reversible chain with a construction that does not increase the asymptotic variance. These modified chains work to avoid diffusive backtracking behavior which causes Markov chains to be trapped in one posit
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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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Pereira, 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.

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Cheal, 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.

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Durmus, Alain. "High dimensional Markov chain Monte Carlo methods : theory, methods and applications." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLT001/document.

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L'objet de cette thèse est l'analyse fine de méthodes de Monte Carlopar chaînes de Markov (MCMC) et la proposition de méthodologies nouvelles pour échantillonner une mesure de probabilité en grande dimension. Nos travaux s'articulent autour de trois grands sujets.Le premier thème que nous abordons est la convergence de chaînes de Markov en distance de Wasserstein. Nous établissons des bornes explicites de convergence géométrique et sous-géométrique. Nous appliquons ensuite ces résultats à l'étude d'algorithmes MCMC. Nous nous intéressons à une variante de l'algorithme de Metropolis-Langevin aj
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Durmus, Alain. "High dimensional Markov chain Monte Carlo methods : theory, methods and applications." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLT001.

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L'objet de cette thèse est l'analyse fine de méthodes de Monte Carlopar chaînes de Markov (MCMC) et la proposition de méthodologies nouvelles pour échantillonner une mesure de probabilité en grande dimension. Nos travaux s'articulent autour de trois grands sujets.Le premier thème que nous abordons est la convergence de chaînes de Markov en distance de Wasserstein. Nous établissons des bornes explicites de convergence géométrique et sous-géométrique. Nous appliquons ensuite ces résultats à l'étude d'algorithmes MCMC. Nous nous intéressons à une variante de l'algorithme de Metropolis-Langevin aj
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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.

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Books on the topic "Markov chain Monte Carlo methods"

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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.

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

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

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

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

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Joseph, 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.

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Gerhard, Winkler. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Berlin: Springer, 2003.

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Winkler, 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.

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Neal, Radford M. Markov chain Monte Carlo methods based on "slicing" the density function. Toronto: University of Toronto, Dept. of Statistics, 1997.

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

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Book chapters on the topic "Markov chain Monte Carlo methods"

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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.

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Barbu, 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.

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Li, 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.

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Ó 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.

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Robert, 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.

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Lange, 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.

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Hörmann, 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.

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Albert, 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.

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Neifer, 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.

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Chib, 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.

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Conference papers on the topic "Markov chain Monte Carlo methods"

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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.

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Wadsley, 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.

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Somersalo, 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.

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Wadsley, 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.

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Gerencser, 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.

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de 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.

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Nabarrete, 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.

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Runnalls, 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.

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van 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.

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Akoum, 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.

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Reports on the topic "Markov chain Monte Carlo methods"

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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), November 2023. http://dx.doi.org/10.2172/2283285.

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Doss, 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.

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Doss, 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.

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Doss, 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.

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Sethuraman, 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.

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Glaser, 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.

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Knopp, 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.

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Warnes, 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.

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Zang, Emma. Bayesian Statistics for Social and Health Scientists in R and Python. Instats Inc., 2023. http://dx.doi.org/10.61700/obtt1o65iw3ui469.

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This seminar will introduce you to Bayesian statistics, which are increasingly popular and offer a powerful alternative to more traditional forms of statistical analysis. Targeted at a social and health science audience, the seminar will cover the fundamentals of Bayesian inference and illustrate a variety of techniques with applied examples of Bayesian regressions and hierarchical models. You will gain an understanding of Markov chain Monte Carlo (MCMC) methods and learn how to develop and validate Bayesian models so that you can apply them in your daily research, with the kinds of intuitive
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Zang, 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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This seminar will introduce you to Bayesian statistics, which are increasingly popular and offer a powerful alternative to more traditional forms of statistical analysis. Targeted at a social and health science audience, the seminar will cover the fundamentals of Bayesian inference and illustrate a variety of techniques with applied examples of Bayesian regressions and hierarchical models. You will gain an understanding of Markov chain Monte Carlo (MCMC) methods and learn how to develop and validate Bayesian models so that you can apply them in your daily research, with the kinds of intuitive
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