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Relevant bibliographies by topics / Gaussian; Markov chain Monte Carlo methods

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

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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

1

Wang, Zheng, Shanxiang Lyu, and Ling Liu. "Learnable Markov Chain Monte Carlo Sampling Methods for Lattice Gaussian Distribution." IEEE Access 7 (2019): 87494–503. http://dx.doi.org/10.1109/access.2019.2925530.

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Ahmadian, Yashar, Jonathan W. Pillow, and Liam Paninski. "Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains." Neural Computation 23, no. 1 (2011): 46–96. http://dx.doi.org/10.1162/neco_a_00059.

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Stimulus reconstruction or decoding methods provide an important tool for understanding how sensory and motor information is represented in neural activity. We discuss Bayesian decoding methods based on an encoding generalized linear model (GLM) that accurately describes how stimuli are transformed into the spike trains of a group of neurons. The form of the GLM likelihood ensures that the posterior distribution over the stimuli that caused an observed set of spike trains is log concave so long as the prior is. This allows the maximum a posteriori (MAP) stimulus estimate to be obtained using e

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Wang, Zheng. "Markov Chain Monte Carlo Methods for Lattice Gaussian Sampling: Convergence Analysis and Enhancement." IEEE Transactions on Communications 67, no. 10 (2019): 6711–24. http://dx.doi.org/10.1109/tcomm.2019.2926470.

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Whiley, Matt, and Simon P. Wilson. "Parallel algorithms for Markov chain Monte Carlo methods in latent spatial Gaussian models." Statistics and Computing 14, no. 3 (2004): 171–79. http://dx.doi.org/10.1023/b:stco.0000035299.51541.5e.

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Geweke, John, and Hisashi Tanizaki. "On markov chain monte carlo methods for nonlinear and non-gaussian state-space models." Communications in Statistics - Simulation and Computation 28, no. 4 (1999): 867–94. http://dx.doi.org/10.1080/03610919908813583.

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Jiao, Zhun, and Rong Zhang. "Improved Particle Filter for Integrated Navigation System." Applied Mechanics and Materials 543-547 (March 2014): 1278–81. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1278.

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As a new method for dealing with any nonlinear or non-Gaussian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters (PF) are favored by researchers and widely applied in many fields. Based on particle filtering, an improved particle filter (IPF) proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for SINS/GPS integrated navigation system. The simulation results confirm that the improved particle filter outperforms th

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Lu, Dan, Daniel Ricciuto, Anthony Walker, Cosmin Safta, and William Munger. "Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods." Biogeosciences 14, no. 18 (2017): 4295–314. http://dx.doi.org/10.5194/bg-14-4295-2017.

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Abstract. Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of

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Durante, Daniele. "Conjugate Bayes for probit regression via unified skew-normal distributions." Biometrika 106, no. 4 (2019): 765–79. http://dx.doi.org/10.1093/biomet/asz034.

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Summary Regression models for dichotomous data are ubiquitous in statistics. Besides being useful for inference on binary responses, these methods serve as building blocks in more complex formulations, such as density regression, nonparametric classification and graphical models. Within the Bayesian framework, inference proceeds by updating the priors for the coefficients, typically taken to be Gaussians, with the likelihood induced by probit or logit regressions for the responses. In this updating, the apparent absence of a tractable posterior has motivated a variety of computational methods,

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Peng, Li Feng, Guo Shao Su, and Wei Zhao. "Fast Analysis of Structural Reliability Using Gaussian Process Classification Based Dynamic Response Surface Method." Applied Mechanics and Materials 501-504 (January 2014): 1067–70. http://dx.doi.org/10.4028/www.scientific.net/amm.501-504.1067.

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The performance function of large-scale complicated engineering structure is always highly nonlinear and implicit, and its reliability needs to be evaluated through a time-consuming Finite Element method (FEM). A new method, Gaussian process classification (GPC) dynamic response surface based on Monte Carlo Simulation (MCS) was proposed. Small training samples were created using FEM and Markov chain. Then, the most probable point (MPP) is predicted quickly using MCS without any extra FEM analysis. Furthermore, an iterative algorithm is presented to reduce the errors of GPC by using information

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Tilmann, F. J., H. Sadeghisorkhani, and A. Mauerberger. "Another look at the treatment of data uncertainty in Markov chain Monte Carlo inversion and other probabilistic methods." Geophysical Journal International 222, no. 1 (2020): 388–405. http://dx.doi.org/10.1093/gji/ggaa168.

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SUMMARY In probabilistic Bayesian inversions, data uncertainty is a crucial parameter for quantifying the uncertainties and correlations of the resulting model parameters or, in transdimensional approaches, even the complexity of the model. However, in many geophysical inference problems it is poorly known. Therefore, it is common practice to allow the data uncertainty itself to be a parameter to be determined. Although in principle any arbitrary uncertainty distribution can be assumed, Gaussian distributions whose standard deviation is then the unknown parameter to be estimated are the usual

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Dissertations / Theses on the topic "Gaussian; Markov chain Monte Carlo methods"

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Manrique, Garcia Aurora. "Econometric analysis of limited dependent time series." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389797.

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Vaičiulytė, Ingrida. "Study and application of Markov chain Monte Carlo method." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20141209_112440-55390.

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Markov chain Monte Carlo adaptive methods by creating computationally effective algorithms for decision-making of data analysis with the given accuracy are analyzed in this dissertation. The tasks for estimation of parameters of the multivariate distributions which are constructed in hierarchical way (skew t distribution, Poisson-Gaussian model, stable symmetric vector law) are described and solved in this research. To create the adaptive MCMC procedure, the sequential generating method is applied for Monte Carlo samples, introducing rules for statistical termination and for sample size regula

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Lopez, lopera Andres Felipe. "Gaussian Process Modelling under Inequality Constraints." Thesis, Lyon, 2019. https://tel.archives-ouvertes.fr/tel-02863891.

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Le conditionnement de processus gaussiens (PG) par des contraintes d’inégalité permet d’obtenir des modèles plus réalistes. Cette thèse s’intéresse au modèle de type PG proposé par maatouk (2015), obtenu par approximation finie, qui garantit que les contraintes sont satisfaites dans tout l’espace. Plusieurs contributions sont apportées. Premièrement, nous étudions l’emploi de méthodes de monte carlo par chaı̂nes de markov pour des lois multinormales tronquées. Elles fournissent un échantillonnage efficacpour des contraintes d’inégalité linéaires. Deuxièmement, nous explorons l’extension du mod

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Dahlin, Johan. "Accelerating Monte Carlo methods for Bayesian inference in dynamical models." Doctoral thesis, Linköpings universitet, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125992.

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Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. In this thesis, we make use of Bayesian statistics to construct probabilistic models given prior information and historical data, which can be used for decision support and predictions. The main obstacle with this approach is that it often results in mathematical problems lacking analytical solutions. T

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Vaičiulytė, Ingrida. "Markovo grandinės Monte-Karlo metodo tyrimas ir taikymas." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20141209_112429-75205.

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Disertacijoje nagrinėjami Markovo grandinės Monte-Karlo (MCMC) adaptavimo metodai, skirti efektyviems skaitiniams duomenų analizės sprendimų priėmimo su iš anksto nustatytu patikimumu algoritmams sudaryti. Suformuluoti ir išspręsti hierarchiniu būdu sudarytų daugiamačių skirstinių (asimetrinio t skirstinio, Puasono-Gauso modelio, stabiliojo simetrinio vektoriaus dėsnio) parametrų vertinimo uždaviniai. Adaptuotai MCMC procedūrai sukurti yra pritaikytas nuoseklaus Monte-Karlo imčių generavimo metodas, įvedant statistinį stabdymo kriterijų ir imties tūrio reguliavimą. Statistiniai uždaviniai išsp

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Puengnim, Anchalee. "Classification de modulations linéaires et non-linéaires à l'aide de méthodes bayésiennes." Toulouse, INPT, 2008. http://ethesis.inp-toulouse.fr/archive/00000676/.

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La reconnaissance de modulations numériques consiste à identifier, au niveau du récepteur d'une chaîne de transmission, l'alphabet auquel appartiennent les symboles du message transmis. Cette reconnaissance est nécessaire dans de nombreux scénarios de communication, afin, par exemple, de sécuriser les transmissions pour détecter d'éventuels utilisateurs non autorisés ou bien encore de déterminer quel terminal brouille les autres. Le signal observé en réception est généralement affecté d'un certain nombre d'imperfections, dues à une synchronisation imparfaite de l'émetteur et du récepteur, une

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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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Books on the topic "Gaussian; Markov chain Monte Carlo methods"

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

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Winkler, Gerhard. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. 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. University of Toronto, Dept. of Statistics, 1997.

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

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1946-, Winkler Gerhard, ed. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Springer, 2003.

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Cheng, Russell. Finite Mixture Examples; MAPIS Details. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0018.

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Two detailed numerical examples are given in this chapter illustrating and comparing mainly the reversible jump Markov chain Monte Carlo (RJMCMC) and the maximum a posteriori/importance sampling (MAPIS) methods. The numerical examples are the well-known galaxy data set with sample size 82, and the Hidalgo stamp issues thickness data with sample size 485. A comparison is made of the estimates obtained by the RJMCMC and MAPIS methods for (i) the posterior k-distribution of the number of components, k, (ii) the predictive finite mixture distribution itself, and (iii) the posterior distributions o

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Carroll, Raymond, Faming Liang, and Chuanhai Liu. Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. Wiley & Sons, Incorporated, John, 2011.

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Carroll, Raymond, Faming Liang, and Chuanhai Liu. Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. Wiley & Sons, Incorporated, John, 2010.

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Book chapters on the topic "Gaussian; 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. 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. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5_8.

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Rizzo, Maria L. "Markov Chain Monte Carlo Methods." In Statistical Computing with R. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192760-11.

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Lange, Kenneth. "Markov Chain Monte Carlo Methods." In Mathematical and Statistical Methods for Genetic Analysis. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2739-5_9.

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Asmussen, Søren, and Peter W. Glynn. "Markov Chain Monte Carlo Methods." In Stochastic Modelling and Applied Probability. Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-69033-9_13.

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Chib, Siddhartha. "Markov Chain Monte Carlo Methods." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2042-1.

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Albert, Jim. "Markov Chain Monte Carlo Methods." In Bayesian Computation with R. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92298-0_6.

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Hörmann, Wolfgang, Josef Leydold, and Gerhard Derflinger. "Markov Chain Monte Carlo Methods." In Automatic Nonuniform Random Variate Generation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05946-3_14.

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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. 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. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1716-9_1.

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

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Pandita, Piyush, Jesper Kristensen, and Liping Wang. "Towards Scalable Gaussian Process Modeling." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97010.

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Abstract Numerous engineering problems of interest to the industry are often characterized by expensive black-box objective function evaluations. These objective functions could be physical experiments or computer simulations. Obtaining a comprehensive idea of the problem and/or performing subsequent optimizations generally requires hundreds of thousands of evaluations of the objective function which is most often a practically unachievable task. Gaussian Process (GP) surrogate modeling replaces the expensive function with a cheap-to-evaluate data-driven probabilistic model. While the GP does

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Khalil, Mohammad, Abhijit Sarkar, and Dominique Poirel. "Application of Bayesian Inference to the Flutter Margin Method: New Developments." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-30041.

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Zimmerman and Weissenburger flutter margin method is extended to account for modal parameter uncertainties by applying a Bayesian estimation technique to obtain the probability distribution function of the flutter speed. In previous work, a least-squares estimation technique was applied to obtain the posterior pdf of the flutter speed. The limitation of this technique is the assumption that the flutter margin at each airspeed is strictly Gaussian. In this paper, the joint distribution of the modal parameters (and consequently the flutter margin) is obtained from preflutter measured system resp

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Gang, Jinhyuk, Jooho Choi, Bonghee Lee, and Jinwon Joo. "Material Parameter Identification of Viscoplastic Model for Solder Alloy in Electronics Package Using Bayesian Calibration." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28603.

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In this study, a method of computer model calibration is applied to quantify the uncertainties arising in the material characterization of the solder joint in the microelectronics package subject to a thermal cycle. In this study, all uncertainties are addressed by using a Bayesian calibration approach. A special specimen that characterizes the solder property due to the shear deformation is prepared, from which the Moire´ fringe is measured by running a thermal cycle. Viscoplastic finite element analysis procedure is constructed for the specimen based on the Anand model. Gaussian process mode

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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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Wang, Zheng, Cong Ling, and Guillaume Hanrot. "Markov chain Monte Carlo algorithms for lattice Gaussian sampling." In 2014 IEEE International Symposium on Information Theory (ISIT). IEEE, 2014. http://dx.doi.org/10.1109/isit.2014.6875081.

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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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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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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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Khalil, Mohammad, Abhijit Sarkar, and Dominique Poirel. "Parameter Estimation of a Fluttering Aeroelastic System in the Transitional Reynolds Number Regime." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-30047.

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We report the parameter estimation results of a self-sustaining aeroelastic oscillator. The system is composed of a rigid wing that is elastically mounted on a rig, which in turn is fixed in a wind tunnel. For certain flow conditions, in particular dictated by the Reynolds number in the transitional regime, the wing extracts energy from the flow leading to a stable limit cycle oscillation. The basic physical mechanism at the origin of the oscillations is laminar boundary layer separation, which leads to negative aerodynamic damping. An empirical model of the aeroelastic system is proposed in t

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Reports on the topic "Gaussian; Markov chain Monte Carlo methods"

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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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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. 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/ada379998.

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Glaser, R., G. Johannesson, S. Sengupta, 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), 2004. http://dx.doi.org/10.2172/15009813.

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