Relevant bibliographies by topics / Metropolis-Hastings algoritm

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Academic literature on the topic 'Metropolis-Hastings algoritm'

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Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Metropolis-Hastings algoritm"

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Delmas, Jean-Françcois, and Benjamin Jourdain. "Does Waste Recycling Really Improve the Multi-Proposal Metropolis–Hastings algorithm? an Analysis Based on Control Variates." Journal of Applied Probability 46, no. 04 (2009): 938–59. http://dx.doi.org/10.1017/s0021900200006069.

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The waste-recycling Monte Carlo (WRMC) algorithm introduced by physicists is a modification of the (multi-proposal) Metropolis–Hastings algorithm, which makes use of all the proposals in the empirical mean, whereas the standard (multi-proposal) Metropolis–Hastings algorithm uses only the accepted proposals. In this paper we extend the WRMC algorithm to a general control variate technique and exhibit the optimal choice of the control variate in terms of the asymptotic variance. We also give an example which shows that, in contradiction to the intuition of physicists, the WRMC algorithm can have
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Delmas, Jean-Françcois, and Benjamin Jourdain. "Does Waste Recycling Really Improve the Multi-Proposal Metropolis–Hastings algorithm? an Analysis Based on Control Variates." Journal of Applied Probability 46, no. 4 (2009): 938–59. http://dx.doi.org/10.1239/jap/1261670681.

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The waste-recycling Monte Carlo (WRMC) algorithm introduced by physicists is a modification of the (multi-proposal) Metropolis–Hastings algorithm, which makes use of all the proposals in the empirical mean, whereas the standard (multi-proposal) Metropolis–Hastings algorithm uses only the accepted proposals. In this paper we extend the WRMC algorithm to a general control variate technique and exhibit the optimal choice of the control variate in terms of the asymptotic variance. We also give an example which shows that, in contradiction to the intuition of physicists, the WRMC algorithm can have
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Hu, Yulin, and Yayong Tang. "Metropolis-Hastings Algorithm with Delayed Acceptance and Rejection." Review of Educational Theory 2, no. 2 (2019): 7. http://dx.doi.org/10.30564/ret.v2i2.682.

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Metropolis-Hastings algorithms are slowed down by the computation of complex target distributions. To solve this problem, one can use the delayed acceptance Metropolis-Hastings algorithm (MHDA) of Christen and Fox (2005). However, the acceptance rate of a proposed value will always be less than in the standard Metropolis-Hastings. We can fix this problem by using the Metropolis-Hastings algorithm with delayed rejection (MHDR) proposed by Tierney and Mira (1999). In this paper, we combine the ideas of MHDA and MHDR to propose a new MH algorithm, named the Metropolis-Hastings algorithm with dela
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Cheon, Soo-Young, and Hee-Chan Lee. "Metropolis-Hastings Expectation Maximization Algorithm for Incomplete Data." Korean Journal of Applied Statistics 25, no. 1 (2012): 183–96. http://dx.doi.org/10.5351/kjas.2012.25.1.183.

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Chib, Siddhartha, and Edward Greenberg. "Understanding the Metropolis-Hastings Algorithm." American Statistician 49, no. 4 (1995): 327. http://dx.doi.org/10.2307/2684568.

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Chib, Siddhartha, and Edward Greenberg. "Understanding the Metropolis-Hastings Algorithm." American Statistician 49, no. 4 (1995): 327–35. http://dx.doi.org/10.1080/00031305.1995.10476177.

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Marnissi, Yosra, Emilie Chouzenoux, Amel Benazza-Benyahia, and Jean-Christophe Pesquet. "Majorize–Minimize Adapted Metropolis–Hastings Algorithm." IEEE Transactions on Signal Processing 68 (2020): 2356–69. http://dx.doi.org/10.1109/tsp.2020.2983150.

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Lemieux, Jessica, Bettina Heim, David Poulin, Krysta Svore, and Matthias Troyer. "Efficient Quantum Walk Circuits for Metropolis-Hastings Algorithm." Quantum 4 (June 29, 2020): 287. http://dx.doi.org/10.22331/q-2020-06-29-287.

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We present a detailed circuit implementation of Szegedy's quantization of the Metropolis-Hastings walk. This quantum walk is usually defined with respect to an oracle. We find that a direct implementation of this oracle requires costly arithmetic operations. We thus reformulate the quantum walk, circumventing its implementation altogether by closely following the classical Metropolis-Hastings walk. We also present heuristic quantum algorithms that use the quantum walk in the context of discrete optimization problems and numerically study their performances. Our numerical results indicate polyn
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Masuhr, Andreas, and Mark Trede. "Bayesian estimation of generalized partition of unity copulas." Dependence Modeling 8, no. 1 (2020): 119–31. http://dx.doi.org/10.1515/demo-2020-0007.

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AbstractThis paper proposes a Bayesian estimation algorithm to estimate Generalized Partition of Unity Copulas (GPUC), a class of nonparametric copulas recently introduced by [18]. The first approach is a random walk Metropolis-Hastings (RW-MH) algorithm, the second one is a random blocking random walk Metropolis-Hastings algorithm (RBRW-MH). Both approaches are Markov chain Monte Carlo methods and can cope with ˛at priors. We carry out simulation studies to determine and compare the efficiency of the algorithms. We present an empirical illustration where GPUCs are used to nonparametrically de
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Saraiva, Erlandson, Adriano Suzuki, and Luis Milan. "Bayesian Computational Methods for Sampling from the Posterior Distribution of a Bivariate Survival Model, Based on AMH Copula in the Presence of Right-Censored Data." Entropy 20, no. 9 (2018): 642. http://dx.doi.org/10.3390/e20090642.

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In this paper, we study the performance of Bayesian computational methods to estimate the parameters of a bivariate survival model based on the Ali–Mikhail–Haq copula with marginal distributions given by Weibull distributions. The estimation procedure was based on Monte Carlo Markov Chain (MCMC) algorithms. We present three version of the Metropolis–Hastings algorithm: Independent Metropolis–Hastings (IMH), Random Walk Metropolis (RWM) and Metropolis–Hastings with a natural-candidate generating density (MH). Since the creation of a good candidate generating density in IMH and RWM may be diffic
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Dissertations / Theses on the topic "Metropolis-Hastings algoritm"

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BARROS, Kleber Napoleão Nunes de Oliveira. "Abordagem clássica e Bayesiana em modelos simétricos transformados aplicados à estimativa de crescimento em altura de Eucalyptus urophylla no Polo Gesseiro do Araripe-PE." Universidade Federal Rural de Pernambuco, 2010. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5142.

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Submitted by (ana.araujo@ufrpe.br) on 2016-08-01T17:35:24Z No. of bitstreams: 1 Kleber Napoleao Nunes de Oliveira Barros.pdf: 2964667 bytes, checksum: a3c757cb7ed16fc9c38b7834b6e0fa29 (MD5)<br>Made available in DSpace on 2016-08-01T17:35:24Z (GMT). No. of bitstreams: 1 Kleber Napoleao Nunes de Oliveira Barros.pdf: 2964667 bytes, checksum: a3c757cb7ed16fc9c38b7834b6e0fa29 (MD5) Previous issue date: 2010-02-22<br>It is presented in this work the growth model nonlinear Chapman-Richards with distribution of errors following the new class of symmetric models processed and Bayesian inference
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Þorgeirsson, Sverrir. "Bayesian parameter estimation in Ecolego using an adaptive Metropolis-Hastings-within-Gibbs algorithm." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-304259.

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Ecolego is scientific software that can be used to model diverse systems within fields such as radioecology and pharmacokinetics. The purpose of this research is to develop an algorithm for estimating the probability density functions of unknown parameters of Ecolego models. In order to do so, a general-purpose adaptive Metropolis-Hastings-within-Gibbs algorithm is developed and tested on some examples of Ecolego models. The algorithm works adequately on those models, which indicates that the algorithm could be integrated successfully into future versions of Ecolego.
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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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Gendre, Victor Hugues. "Predicting short term exchange rates with Bayesian autoregressive state space models: an investigation of the Metropolis Hastings algorithm forecasting efficiency." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437399395.

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Cercone, Maria Grazia. "Origini e sviluppi del calcolo delle probabilità ed alcune applicazioni." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13500/.

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Questa tesi ripercorre l’evoluzione del calcolo delle probabilità attraverso i secoli partendo dalle origini e giungendo al XX secolo, segnato dall’introduzione della teoria sulle Catene di Markov che, fornendo importanti concetti matematici, troverà in seguito applicazione in ambiti diversi tra loro. Il primo capitolo è interamente dedicato ad illustrare l’excursus storico del calcolo delle probabilità; si parte dalle antiche civiltà, dove l’idea di probabilità sorse intorno a questioni riguardanti la vita comune e il gioco d’azzardo, per arrivare al XX secolo in cui si formarono le tre scuol
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Volfson, Alexander. "Exploring the optimal Transformation for Volatility." Digital WPI, 2010. https://digitalcommons.wpi.edu/etd-theses/472.

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This paper explores the fit of a stochastic volatility model, in which the Box-Cox transformation of the squared volatility follows an autoregressive Gaussian distribution, to the continuously compounded daily returns of the Australian stock index. Estimation was difficult, and over-fitting likely, because more variables are present than data. We developed a revised model that held a couple of these variables fixed and then, further, a model which reduced the number of variables significantly by grouping trading days. A Metropolis-Hastings algorithm was used to simulate the joint density and d
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VASCONCELOS, Josimar Mendes de. "Equações simultâneas no contexto clássico e bayesiano: uma abordagem à produção de soja." Universidade Federal Rural de Pernambuco, 2011. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5012.

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Submitted by (ana.araujo@ufrpe.br) on 2016-07-07T12:44:03Z No. of bitstreams: 1 Josimar Mendes de Vasconcelos.pdf: 4725831 bytes, checksum: 716f4b6bc6100003772271db252915b7 (MD5)<br>Made available in DSpace on 2016-07-07T12:44:03Z (GMT). No. of bitstreams: 1 Josimar Mendes de Vasconcelos.pdf: 4725831 bytes, checksum: 716f4b6bc6100003772271db252915b7 (MD5) Previous issue date: 2011-08-08<br>Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq<br>The last years has increased the quantity of researchers and search scientific in the plantation, production and val
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Merhi, Bleik Josephine. "Modeling, estimation and simulation into two statistical models : quantile regression and blind deconvolution." Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2506.

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Cette thèse est consacrée à l’estimation de deux modèles statistiques : le modèle des quantiles de régression simultanés et le modèle de déconvolution aveugle. Elle se compose donc de deux parties. Dans la première partie, nous nous intéressons à l’estimation simultanée de plusieurs quantiles de régression par l’approche Bayésienne. En supposant que le terme d’erreur suit la distribution de Laplace asymétrique et en utilisant la relation entre deux quantiles distincts de cette distribution, nous proposons une méthode simple entièrement Bayésienne qui satisfait la propriété non croisée des quan
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Alves, Andressa Schneider. "Algoritmos para o encaixe de moldes com formato irregular em tecidos listrados." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/142744.

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Esta tese tem como objetivo principal a proposição de solução para o problema do encaixe de moldes em tecidos listrados da indústria do vestuário. Os moldes são peças com formato irregular que devem ser dispostos sobre a matéria-prima, neste caso o tecido, para a etapa posterior de corte. No problema específico do encaixe em tecidos listrados, o local em que os moldes são posicionados no tecido deve garantir que, após a confecção da peça, as listras apresentem continuidade. Assim, a fundamentação teórica do trabalho abrange temas relacionados à moda e ao design do vestuário, como os tipos e pa
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Zeppilli, Giulia. "Alcune applicazioni del Metodo Monte Carlo." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3091/.

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Book chapters on the topic "Metropolis-Hastings algoritm"

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Robert, Christian P., and George Casella. "Metropolis–Hastings Algorithms." In Introducing Monte Carlo Methods with R. Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1576-4_6.

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Robert, Christian P., and George Casella. "The Metropolis—Hastings Algorithm." In Springer Texts in Statistics. Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4145-2_7.

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Robert, Christian P., and George Casella. "The Metropolis—Hastings Algorithm." In Springer Texts in Statistics. Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4757-3071-5_6.

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Arcos-Argudo, Miguel, Rodolfo Bojorque-Chasi, and Andrea Plaza-Cordero. "Random Samplings Using Metropolis Hastings Algorithm." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20454-9_11.

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Hoff, Peter D. "Nonconjugate priors and Metropolis-Hastings algorithms." In Springer Texts in Statistics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92407-6_10.

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Chowdhury, Asif, and Gabriel Terejanu. "An Enhanced Metropolis-Hastings Algorithm Based on Gaussian Processes." In Model Validation and Uncertainty Quantification, Volume 3. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29754-5_22.

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Dammalapati, Sai Krishna, Vishal Murmu, and Gnanasekaran Nagarajan. "Bayesian Inference Approach to Estimate Robin Coefficient Using Metropolis Hastings Algorithm." In Recent Advances in Chemical Engineering. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1633-2_32.

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Chelbi, Nacer Eddine, Denis Gingras, and Claude Sauvageau. "New Field Operational Tests Sampling Strategy Based on Metropolis-Hastings Algorithm." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01054-6_90.

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Płoński, Piotr, and Krzysztof Zaremba. "Self-Organising Maps for Classification with Metropolis-Hastings Algorithm for Supervision." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34487-9_19.

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Kirimasthong, Khwunta, Aompilai Manorat, Jeerayut Chaijaruwanich, Sukon Prasitwattanaseree, and Chinae Thammarongtham. "Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73871-8_26.

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Conference papers on the topic "Metropolis-Hastings algoritm"

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Luengo, David, and Luca Martino. "Fully adaptive Gaussian mixture Metropolis-Hastings algorithm." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638846.

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Yang, Yanfang, Yanjie Zhang, Yingjun Zhou, and Wenhua Zhang. "An Improved Metropolis-Hastings Algorithm Based on Particle Filter." In 2009 IITA International Conference on Control, Automation and Systems Engineering, CASE 2009. IEEE, 2009. http://dx.doi.org/10.1109/case.2009.148.

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Wang, Zheng, and Cong Ling. "Independent Metropolis-Hastings-Klein algorithm for lattice Gaussian sampling." In 2015 IEEE International Symposium on Information Theory (ISIT). IEEE, 2015. http://dx.doi.org/10.1109/isit.2015.7282900.

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Kenyeres, Martin, and Jozef Kenyeres. "Applicability of Generalized Metropolis-Hastings Algorithm in Wireless Sensor Networks." In IEEE EUROCON 2019 -18th International Conference on Smart Technologies. IEEE, 2019. http://dx.doi.org/10.1109/eurocon.2019.8861554.

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El Chamie, Mahmoud, and Behcet Acikmese. "Robust metropolis-hastings algorithm for safe reversible Markov chain synthesis." In 2016 American Control Conference (ACC). IEEE, 2016. http://dx.doi.org/10.1109/acc.2016.7526795.

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Bidyuk, Peter, Volodymyr Beglytsia, Aleksandr Gozhyj, and Irina Kalinina. "Using the Metropolis-Hastings algorithm in Bayesian data analysis procedures." In 2019 IEEE 14th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT). IEEE, 2019. http://dx.doi.org/10.1109/stc-csit.2019.8929797.

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Hague, Stephen, and Simaan AbouRizk. "Nonparametric frequency polygon estimation for modeling input data." In The 19th International Conference on Modelling and Applied Simulation. CAL-TEK srl, 2019. http://dx.doi.org/10.46354/i3m.2019.mas.020.

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To construct valid probability distributions solely from input data, this paper compares three nonparametric density estimators: (1) histograms, (2) Kernel Density Estimation, and (3) Frequency Polygon Estimation. A pseudocode is implemented, a practical example is illustrated, and the Simphony.NET simulation environment is used to fit the nonparametric frequency polygon to a set of data to recreate it as a posterior distribution via the Metropolis-Hastings algorithm.
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Hou, Yunshan, and Jianguo Huang. "Fast Algorithm for Bayesian DOA Estimator Based on Metropolis-Hastings Sampling." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.113.

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Kenyeres, M., and J. Kenyeres. "Generalized Metropolis-Hastings Algorithm for Distributed Averaging with Uniform Quantization Scheme." In 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA). IEEE, 2019. http://dx.doi.org/10.1109/iceta48886.2019.9039974.

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Sahin, Bugrahan, and Ali Taylan Cemgil. "Approaches for reducing the computational budget of the metropolis hastings algorithm." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404367.

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