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Data publikacji: 4 czerwca 2021

Data aktualizacji: 12 października 2024

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Paul, Rajib. "Theoretical And Algorithmic Developments In Markov Chain Monte Carlo." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218184168.

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12

Khedri, Shiler. "Markov chain Monte Carlo methods for exact tests in contingency tables." Thesis, Durham University, 2012. http://etheses.dur.ac.uk/5579/.

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This thesis is mainly concerned with conditional inference for contingency tables, where the MCMC method is used to take a sample of the conditional distribution. One of the most common models to be investigated in contingency tables is the independence model. Classic test statistics for testing the independence hypothesis, Pearson and likelihood chi-square statistics rely on large sample distributions. The large sample distribution does not provide a good approximation when the sample size is small. The Fisher exact test is an alternative method which enables us to compute the exact p-value f

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13

Ibrahim, Adriana Irawati Nur. "New methods for mode jumping in Markov chain Monte Carlo algorithms." Thesis, University of Bath, 2009. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500720.

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Standard Markov chain Monte Carlo (MCMC) sampling methods can experience problem sampling from multi-modal distributions. A variety of sampling methods have been introduced to overcome this problem. The mode jumping method of Tjelmeland & Hegstad (2001) tries to find a mode and propose a value from that mode in each mode jumping attempt. This approach is inefficient in that the work needed to find each mode and model the distribution in a neighbourhood of the mode is carried out repeatedly during the sampling process. We shall propose a new mode jumping approach which retains features of the T

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14

Barata, Teresa Cordeiro Ferreira Nunes. "Two examples of curve estimation using Markov Chain Monte Carlo methods." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612139.

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15

Nemirovsky, Danil. "Monte Carlo methods and Markov chain based approaches for PageRank computation." Nice, 2010. http://www.theses.fr/2010NICE4018.

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16

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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17

Gausland, Eivind Blomholm. "Parameter Estimation in Extreme Value Models with Markov Chain Monte Carlo Methods." Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10032.

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<p>In this thesis I have studied how to estimate parameters in an extreme value model with Markov Chain Monte Carlo (MCMC) given a data set. This is done with synthetic Gaussian time series generated by spectral densities, called spectrums, with a "box" shape. Three different spectrums have been used. In the acceptance probability in the MCMC algorithm, the likelihood have been built up by dividing the time series into blocks consisting of a constant number of points. In each block, only the maximum value, i.e. the extreme value, have been used. Each extreme value will then be interpreted as i

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18

Niederberger, Theresa. "Markov chain Monte Carlo methods for parameter identification in systems biology models." Diss., Ludwig-Maximilians-Universität München, 2012. http://nbn-resolving.de/urn:nbn:de:bvb:19-157798.

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19

Xu, Xiaojin. "Methods in Hypothesis Testing, Markov Chain Monte Carlo and Neuroimaging Data Analysis." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10927.

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This thesis presents three distinct topics: a modified K-S test for autocorrelated data, improving MCMC convergence rate with residual augmentations, and resting state fMRI data analysis. In Chapter 1, we present a modified K-S test to adjust for sample autocorrelation. We first demonstrate that the original K-S test does not have the nominal type one error rate when applied to autocorrelated samples. Then the notion of mixing conditions and Billingsley's theorem are reviewed. Based on these results, we suggest an effective sample size formula to adjust sample autocorrelation. Extensive simula

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20

Demiris, Nikolaos. "Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods." Thesis, University of Nottingham, 2004. http://eprints.nottingham.ac.uk/10078/.

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This thesis is concerned with statistical methodology for the analysis of stochastic SIR (Susceptible->Infective->Removed) epidemic models. We adopt the Bayesian paradigm and we develop suitably tailored Markov chain Monte Carlo (MCMC) algorithms. The focus is on methods that are easy to generalise in order to accomodate epidemic models with complex population structures. Additionally, the models are general enough to be applicable to a wide range of infectious diseases. We introduce the stochastic epidemic models of interest and the MCMC methods we shall use and we review existing methods of

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21

Spade, David Allen. "Investigating Convergence of Markov Chain Monte Carlo Methods for Bayesian Phylogenetic Inference." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1372173121.

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22

Li, Yuanzhi. "Bayesian Models for Repeated Measures Data Using Markov Chain Monte Carlo Methods." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/6997.

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Bayesian models for repeated measures data are fitted to three different data an analysis projects. Markov Chain Monte Carlo (MCMC) methodology is applied to each case with Gibbs sampling and / or an adaptive Metropolis-Hastings (MH ) algorithm used to simulate the posterior distribution of parameters. We implement a Bayesian model with different variance-covariance structures to an audit fee data set. Block structures and linear models for variances are used to examine the linear trend and different behaviors before and after regulatory change during year 2004-2005. We proposed a Bayesian hie

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23

Browne, William J. "Applying MCMC methods to multi-level models." Thesis, University of Bath, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268210.

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24

Tu, Zhuowen. "Image Parsing by Data-Driven Markov Chain Monte Carlo." The Ohio State University, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=osu1038347031.

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25

Veitch, John D. "Applications of Markov Chain Monte Carlo methods to continuous gravitational wave data analysis." Thesis, Connect to e-thesis to view abstract. Move to record for print version, 2007. http://theses.gla.ac.uk/35/.

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Thesis (Ph.D.) - University of Glasgow, 2007.<br>Ph.D. thesis submitted to Information and Mathematical Sciences Faculty, Department of Mathematics, University of Glasgow, 2007. Includes bibliographical references. Print version also available.

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26

Walker, Neil Rawlinson. "A Bayesian approach to the job search model and its application to unemployment durations using MCMC methods." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299053.

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27

Higdon, David. "Spatial applications of Markov chain Monte Carlo for Bayesian inference /." Thesis, Connect to this title online; UW restricted, 1994. http://hdl.handle.net/1773/8942.

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28

Olvera, Astivia Oscar Lorenzo. "On the estimation of the polychoric correlation coefficient via Markov Chain Monte Carlo methods." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44349.

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Bayesian statistics is an alternative approach to traditional frequentist statistics that is rapidly gaining adherents across different scientific fields. Although initially only accessible to statisticians or mathematically-sophisticated data analysts, advances in modern computational power are helping to make this new paradigm approachable to the everyday researcher and this dissemination is helping open doors to problems that have remained unsolvable or whose solution was extremely complicated through the use of classical statistics. In spite of this, many researchers in the behavioural or

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29

Bray, Isabelle Cella. "Modelling the prevalence of Down syndrome with applications of Markov chain Monte Carlo methods." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/2408.

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This thesis was motivated by applications in the epidemiology of Down syndrome and prenatal screening for Down syndrome. Methodological problems arising in these applications include under-ascertainment of cases in livebirth studies, double-sampled data with missing observations and coarsening of data. These issues are considered from a classical perspective using maximum likelihood and from a Bayesian viewpoint employing Markov chain Monte Carlo (MCMC) techniques. Livebirth prevalence studies published in the literature used a variety of data collection methods and many are of uncertain compl

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30

Witte, Hugh Douglas. "Markov chain Monte Carlo and data augmentation methods for continuous-time stochastic volatility models." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/283976.

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In this paper we exploit some recent computational advances in Bayesian inference, coupled with data augmentation methods, to estimate and test continuous-time stochastic volatility models. We augment the observable data with a latent volatility process which governs the evolution of the data's volatility. The level of the latent process is estimated at finer increments than the data are observed in order to derive a consistent estimator of the variance over each time period the data are measured. The latent process follows a law of motion which has either a known transition density or an appr

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31

Byers, Simon. "Bayesian modeling of highly structured systems using Markov chain Monte Carlo /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/8980.

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32

Olsen, Andrew Nolan. "When Infinity is Too Long to Wait: On the Convergence of Markov Chain Monte Carlo Methods." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1433770406.

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33

Karawatzki, Roman, Josef Leydold, and Klaus Pötzelberger. "Automatic Markov Chain Monte Carlo Procedures for Sampling from Multivariate Distributions." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1400/1/document.pdf.

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Generating samples from multivariate distributions efficiently is an important task in Monte Carlo integration and many other stochastic simulation problems. Markov chain Monte Carlo has been shown to be very efficient compared to "conventional methods", especially when many dimensions are involved. In this article we propose a Hit-and-Run sampler in combination with the Ratio-of-Uniforms method. We show that it is well suited for an algorithm to generate points from quite arbitrary distributions, which include all log-concave distributions. The algorithm works automatically in the sense that

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34

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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35

Li, Min. "Bayesian discovery of regulatory motifs using reversible jump Markov chain Monte Carlo /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/9529.

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36

Nilsson, Mats. "Building Reconstruction of Digital Height Models with the Markov Chain Monte Carlo Method." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148886.

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Data about the earth is increasing in value and demand from customers, but itis difficult to produce accurately and cheap. This thesis examines if it is possible to take low resolution and distorted 3D data and increase the accuracy of building geometry by performing building reconstruction. Building reconstruction is performed with a Markov chain Monte Carlo method where building primitives are placed iteratively until a good fit is found. The digital height model and pixel classification used is produced by Vricon. The method is able to correctly place primitive models, but often overestimat

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37

Fu, Jianlin. "A markov chain monte carlo method for inverse stochastic modeling and uncertainty assessment." Doctoral thesis, Universitat Politècnica de València, 2008. http://hdl.handle.net/10251/1969.

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Unlike the traditional two-stage methods, a conditional and inverse-conditional simulation approach may directly generate independent, identically distributed realizations to honor both static data and state data in one step. The Markov chain Monte Carlo (McMC) method was proved a powerful tool to perform such type of stochastic simulation. One of the main advantages of the McMC over the traditional sensitivity-based optimization methods to inverse problems is its power, flexibility and well-posedness in incorporating observation data from different sources. In this work, an improved version o

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38

Lindahl, John, and Douglas Persson. "Data-driven test case design of automatic test cases using Markov chains and a Markov chain Monte Carlo method." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43498.

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Large and complex software that is frequently changed leads to testing challenges. It is well established that the later a fault is detected in software development, the more it costs to fix. This thesis aims to research and develop a method of generating relevant and non-redundant test cases for a regression test suite, to catch bugs as early in the development process as possible. The research was executed at Axis Communications AB with their products and systems in mind. The approach utilizes user data to dynamically generate a Markov chain model and with a Markov chain Monte Carlo method,

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39

Fischer, Alexander. "An Uncoupling Coupling method for Markov chain Monte Carlo simulations with an application to biomolecules." [S.l. : s.n.], 2003. http://www.diss.fu-berlin.de/2003/234/index.html.

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40

Yang, Chao. "ON PARTICLE METHODS FOR UNCERTAINTY QUANTIFICATION IN COMPLEX SYSTEMS." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511967797285962.

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41

Lewis, John Robert. "Bayesian Restricted Likelihood Methods." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1407505392.

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42

Walker, Matthew James. "Methods for Bayesian inversion of seismic data." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10504.

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The purpose of Bayesian seismic inversion is to combine information derived from seismic data and prior geological knowledge to determine a posterior probability distribution over parameters describing the elastic and geological properties of the subsurface. Typically the subsurface is modelled by a cellular grid model containing thousands or millions of cells within which these parameters are to be determined. Thus such inversions are computationally expensive due to the size of the parameter space (being proportional to the number of grid cells) over which the posterior is to be determined.

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43

Hörmann, Wolfgang, and Josef Leydold. "Monte Carlo Integration Using Importance Sampling and Gibbs Sampling." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1642/1/document.pdf.

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To evaluate the expectation of a simple function with respect to a complicated multivariate density Monte Carlo integration has become the main technique. Gibbs sampling and importance sampling are the most popular methods for this task. In this contribution we propose a new simple general purpose importance sampling procedure. In a simulation study we compare the performance of this method with the performance of Gibbs sampling and of importance sampling using a vector of independent variates. It turns out that the new procedure is much better than independent importance sampling; up to dimen

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44

Niederberger, Theresa [Verfasser], and Patrick [Akademischer Betreuer] Cramer. "Markov chain Monte Carlo methods for parameter identification in systems biology models / Theresa Niederberger. Betreuer: Patrick Cramer." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2012. http://d-nb.info/1036101029/34.

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45

Matsumoto, Nobuyuki. "Geometry of configuration space in Markov chain Monte Carlo methods and the worldvolume approach to the tempered Lefschetz thimble method." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263464.

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46

Allchin, Lorraine Doreen May. "Statistical methods for mapping complex traits." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:65f392ba-1b64-4b00-8871-7cee98809ce1.

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The first section of this thesis addresses the problem of simultaneously identifying multiple loci that are associated with a trait, using a Bayesian Markov Chain Monte Carlo method. It is applicable to both case/control and quantitative data. I present simulations comparing the methods to standard frequentist methods in human case/control and mouse QTL datasets, and show that in the case/control simulations the standard frequentist method out performs my model for all but the highest effect simulations and that for the mouse QTL simulations my method performs as well as the frequentist method

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47

Tiboaca, Oana D. "On the application of the reversible jump Markov chain Monte Carlo method within structural dynamics." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/17126/.

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System Identification (SID) is an important area of structural dynamics and is concerned with constructing a functional relationship between the inputs and the outputs of a system. Furthermore, it estimates the parameters that the studied system depends upon. This aspect of structural dynamics has been studied for many years and computational methods have been developed in order to deal with the system identification of real structures, with the aim of getting a better understanding of their dynamic behaviour. The most straightforward classification of structures is into structures that behave

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48

Dhulipala, Lakshmi Narasimha Somayajulu. "Bayesian Methods for Intensity Measure and Ground Motion Selection in Performance-Based Earthquake Engineering." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/88493.

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The objective of quantitative Performance-Based Earthquake Engineering (PBEE) is designing buildings that meet the specified performance objectives when subjected to an earthquake. One challenge to completely relying upon a PBEE approach in design practice is the open-ended nature of characterizing the earthquake ground motion by selecting appropriate ground motions and Intensity Measures (IM) for seismic analysis. This open-ended nature changes the quantified building performance depending upon the ground motions and IMs selected. So, improper ground motion and IM selection can lead to errors

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49

Wang, Yinglu. "A Markov Chain Based Method for Time Series Data Modeling and Prediction." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592395278430805.

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50

Chen, Yuting. "Inférence bayésienne dans les modèles de croissance de plantes pour la prévision et la caractérisation des incertitudes." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2014. http://www.theses.fr/2014ECAP0040/document.

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La croissance des plantes en interaction avec l'environnement peut être décrite par des modèles mathématiques. Ceux-ci présentent des perspectives prometteuses pour un nombre considérable d'applications telles que la prévision des rendements ou l'expérimentation virtuelle dans le contexte de la sélection variétale. Dans cette thèse, nous nous intéressons aux différentes solutions capables d'améliorer les capacités prédictives des modèles de croissance de plantes, en particulier grâce à des méthodes statistiques avancées. Notre contribution se résume en quatre parties.Tout d'abord, nous proposo

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