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Relevant bibliographies by topics / Markov chain Monte Carlo (MCMC) / Dissertations / Theses

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Dissertations / Theses on the topic 'Markov chain Monte Carlo (MCMC)'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Guha, Subharup. "Benchmark estimation for Markov Chain Monte Carlo samplers." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1085594208.

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2

Angelino, Elaine Lee. "Accelerating Markov chain Monte Carlo via parallel predictive prefetching." Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:13070022.

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We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. This dissertation demonstrates that MCMC inference can be accelerated in a model of parallel computation that uses speculation to predict and complete computational work ahead of when it is known to be useful. By exploiting fast, iterative approximations to the target density, we can speculatively evaluate many potential future steps of the chain in parallel. In Bayesian inference problems, this approach can accelerate sampling from the target distribution, without compromis

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3

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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4

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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5

Harkness, Miles Adam. "Parallel simulation, delayed rejection and reversible jump MCMC for object recognition." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324266.

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6

Smith, Corey James. "Exact Markov Chain Monte Carlo with Likelihood Approximations for Functional Linear Models." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531833318013379.

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7

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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8

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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9

Jeon, Juncheol. "Deterioration model for ports in the Republic of Korea using Markov chain Monte Carlo with multiple imputation." Thesis, University of Dundee, 2019. https://discovery.dundee.ac.uk/en/studentTheses/1cc538ea-1468-4d51-bcf8-711f8b9912f9.

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Condition of infrastructure is deteriorated over time as it gets older. It is the deterioration model that predicts how and when facilities will deteriorate over time. In most infrastructure management system, the deterioration model is a crucial element. Using the deterioration model, it is very helpful to estimate when repair will be carried out, how much will be needed for the maintenance of the entire facilities, and what maintenance costs will be required during the life cycle of the facility. However, the study of deterioration model for civil infrastructures of ports is still in its inf

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10

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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11

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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12

Lindsten, Fredrik. "Particle filters and Markov chains for learning of dynamical systems." Doctoral thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97692.

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Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods.Particular emphasis is placed on the combination of SMC and MCMC in so called particle MCMC algorithms. These algorithms rely on SMC for generating samples from the often highly autocorrelated state-trajectory. A specific particle MCMC algorithm, referred to as particle Gibbs

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13

Byng, Martyn Charles. "A statistical model for locating regulatory regions in novel DNA sequences." Thesis, University of Reading, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369119.

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14

Frühwirth-Schnatter, Sylvia. "MCMC Estimation of Classical and Dynamic Switching and Mixture Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/698/1/document.pdf.

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In the present paper we discuss Bayesian estimation of a very general model class where the distribution of the observations is assumed to depend on a latent mixture or switching variable taking values in a discrete state space. This model class covers e.g. finite mixture modelling, Markov switching autoregressive modelling and dynamic linear models with switching. Joint Bayesian estimation of all latent variables, model parameters and parameters determining the probability law of the switching variable is carried out by a new Markov Chain Monte Carlo method called permutation sampling. Estima

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15

Karagiannis, Georgios. "AISRJMCMC - Annealed Importance Sampling within Reversible Jump Markov Chain Monte Carlo algorithm : a pseudo-marginal reversible jump MCMC algorithm." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546223.

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16

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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17

Cauchemez, Simon. "Estimation des paramètres de transmission dans les modèles épidémiques par échantillonnage de Monte Carlo par chaine de Markov." Paris 6, 2005. http://www.theses.fr/2005PA066572.

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18

Loza, Reyes Elisa. "Classification of phylogenetic data via Bayesian mixture modelling." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519916.

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Conventional probabilistic models for phylogenetic inference assume that an evolutionary tree,andasinglesetofbranchlengthsandstochasticprocessofDNA evolutionare sufficient to characterise the generating process across an entire DNA alignment. Unfortunately such a simplistic, homogeneous formulation may be a poor description of reality when the data arise from heterogeneous processes. A well-known example is when sites evolve at heterogeneous rates. This thesis is a contribution to the modelling and understanding of heterogeneityin phylogenetic data. Weproposea methodfor the classificationof DN

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19

Garzon, Rozo Betty Johanna. "Modelling operational risk using skew t-copulas and Bayesian inference." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/25751.

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Operational risk losses are heavy tailed and are likely to be asymmetric and extremely dependent among business lines/event types. The analysis of dependence via copula models has been focussed on the bivariate case mainly. In the vast majority of instances symmetric elliptical copulas are employed to model dependence for severities. This thesis proposes a new methodology to assess, in a multivariate way, the asymmetry and extreme dependence between severities, and to calculate the capital for operational risk. This methodology simultaneously uses (i) several parametric distributions and an al

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20

Miazhynskaia, Tatiana, Sylvia Frühwirth-Schnatter, and Georg Dorffner. "A comparison of Bayesian model selection based on MCMC with an application to GARCH-type models." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/586/1/document.pdf.

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This paper presents a comprehensive review and comparison of five computational methods for Bayesian model selection, based on MCMC simulations from posterior model parameter distributions. We apply these methods to a well-known and important class of models in financial time series analysis, namely GARCH and GARCH-t models for conditional return distributions (assuming normal and t-distributions). We compare their performance vis--vis the more common maximum likelihood-based model selection on both simulated and real market data. All five MCMC methods proved feasible in both cases, although d

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21

Delescluse, Matthieu. "Une approche Monte Carlo par chaînes de Markov pour la classification des potentiels d' action : application à l' étude des corrélations d' activité des cellules de Purkinje." Paris 6, 2005. http://www.theses.fr/2005PA066493.

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22

Quiroz, Matias. "Bayesian Inference in Large Data Problems." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-118836.

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In the last decade or so, there has been a dramatic increase in storage facilities and the possibility of processing huge amounts of data. This has made large high-quality data sets widely accessible for practitioners. This technology innovation seriously challenges traditional modeling and inference methodology. This thesis is devoted to developing inference and modeling tools to handle large data sets. Four included papers treat various important aspects of this topic, with a special emphasis on Bayesian inference by scalable Markov Chain Monte Carlo (MCMC) methods. In the first paper, we pr

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23

Wang, Xiao. "Computational Modeling for Differential Analysis of RNA-seq and Methylation data." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/72271.

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Computational systems biology is an inter-disciplinary field that aims to develop computational approaches for a system-level understanding of biological systems. Advances in high-throughput biotechnology offer broad scope and high resolution in multiple disciplines. However, it is still a major challenge to extract biologically meaningful information from the overwhelming amount of data generated from biological systems. Effective computational approaches are of pressing need to reveal the functional components. Thus, in this dissertation work, we aim to develop computational approaches for d

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24

Alasseur, Clémence. "Signaux à propriétés markoviennes sous-jacentes et leur utilisation pour la modélisation de l'atténuation dans les transmissions satellites mobiles en bandes Ku et Ka." Paris 11, 2005. http://www.theses.fr/2005PA112216.

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La demande croissante de services satellites en bande large ainsi que la congestion des systèmes opérant aux fréquences traditionnelles poussent à envisager de nouveaux systèmes à des bandes de transmission supérieures à 10 GHz. Mais les comportements du LMSC (Land Mobile Satellite Channel) sont alors mal connus et nécessitent de tenir compte des perturbations atmosphériques, notamment la pluie. La mobilité des antennes pour l'émission et la réception fait également partie des problèmes des services satellites. Pour concevoir de nouveaux systèmes et des méthodes de compensation des pertes par

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25

Krometis, Justin. "A Bayesian Approach to Estimating Background Flows from a Passive Scalar." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83783.

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We consider the statistical inverse problem of estimating a background flow field (e.g., of air or water) from the partial and noisy observation of a passive scalar (e.g., the concentration of a pollutant). Here the unknown is a vector field that is specified by large or infinite number of degrees of freedom. We show that the inverse problem is ill-posed, i.e., there may be many or no background flows that match a given set of observations. We therefore adopt a Bayesian approach, incorporating prior knowledge of background flows and models of the observation error to develop probabilistic est

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26

Deng, Wei. "Multiple imputation for marginal and mixed models in longitudinal data with informative missingness." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1126890027.

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Thesis (Ph. D.)--Ohio State University, 2005.<br>Title from first page of PDF file. Document formatted into pages; contains xiii, 108 p.; also includes graphics. Includes bibliographical references (p. 104-108). Available online via OhioLINK's ETD Center

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27

Bekhti, Yousra. "Contributions to sparse source localization for MEG/EEG brain imaging." Electronic Thesis or Diss., Paris, ENST, 2018. http://www.theses.fr/2018ENST0017.

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Cette thèse a développé des méthodes parcimonieuses pour la localisation de sources en magnétoencéphalographie (MEG) et l'électroencéphalographie (EEG). Pour un champ électromagnétique donné, il y a un nombre infini de sources réparties à l’intérieur du cerveau qui aurait pu le créer. Cela signifie que le problème inverse est mal-posé, ayant de nombreuses possibles solutions. Cela nous contraint à faire des hypothèses ou des apriori sur le problème. Cette thèse a étudié les méthodes parcimonieuses, i.e., seulement quelques sources focales sont activées lors d’une tâche précise. La première con

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28

Shi, Xu. "Bayesian Modeling for Isoform Identification and Phenotype-specific Transcript Assembly." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/79772.

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The rapid development of biotechnology has enabled researchers to collect high-throughput data for studying various biological processes at the genomic level, transcriptomic level, and proteomic level. Due to the large noise in the data and the high complexity of diseases (such as cancer), it is a challenging task for researchers to extract biologically meaningful information that can help reveal the underlying molecular mechanisms. The challenges call for more efforts in developing efficient and effective computational methods to analyze the data at different levels so as to understand the bi

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Tsai, Tsung-Heng. "Bayesian Alignment Model for Analysis of LC-MS-based Omic Data." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64151.

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Liquid chromatography coupled with mass spectrometry (LC-MS) has been widely used in various omic studies for biomarker discovery. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time alignment is one of the most important yet challenging preprocessing steps, in order to ensure that ion intensity measurements among multiple LC-MS runs are comparable. In this dissertation, we propose a Bayesian alignment model (BAM) for analysis of LC-MS data. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model par

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30

Kastner, Gregor, and Sylvia Frühwirth-Schnatter. "Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models." WU Vienna University of Economics and Business, 2013. http://epub.wu.ac.at/3771/1/paper.pdf.

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Bayesian inference for stochastic volatility models using MCMC methods highly depends on actual parameter values in terms of sampling efficiency. While draws from the posterior utilizing the standard centered parameterization break down when the volatility of volatility parameter in the latent state equation is small, non-centered versions of the model show deficiencies for highly persistent latent variable series. The novel approach of ancillarity-sufficiency interweaving has recently been shown to aid in overcoming these issues for a broad class of multilevel models. In this paper, we

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SILVA, José Rodrigo Santos. "Otimização do método área-velocidade para estimação de vazão fluvial usando MCMC." Universidade Federal Rural de Pernambuco, 2011. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5010.

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Submitted by (ana.araujo@ufrpe.br) on 2016-07-07T12:07:15Z No. of bitstreams: 1 Jose Rodrigo Santos Silva.pdf: 4054411 bytes, checksum: c22cd915da573fd5a5ce7b45feb85a0f (MD5)<br>Made available in DSpace on 2016-07-07T12:07:15Z (GMT). No. of bitstreams: 1 Jose Rodrigo Santos Silva.pdf: 4054411 bytes, checksum: c22cd915da573fd5a5ce7b45feb85a0f (MD5) Previous issue date: 2011-02-18<br>The velocity-area method is a standard procedure for measurement of river discharge, with wide application in hydrometric studies, standardized at the international level by the norm ISO 748:2007 of the Inte

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Inhasz, Juliana. "Term structure dynamics and no-arbitrage under the Taylor Rule." reponame:Repositório Institucional do FGV, 2009. http://hdl.handle.net/10438/4253.

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Made available in DSpace on 2010-04-20T20:58:17Z (GMT). No. of bitstreams: 5 Juliana_Inhazs.pdf.jpg: 19103 bytes, checksum: 3eae73777efe179efa4f61323cb6007a (MD5) Juliana_Inhazs.pdf.txt: 56465 bytes, checksum: 6aa6f5ecdab4f2116adba5b71f4a111d (MD5) license.txt: 4810 bytes, checksum: be830a7f281cc1281c4d014095a9ff87 (MD5) Juliana_Inhazs.pdf: 234479 bytes, checksum: 982e4b9180d265432c016715b7246a7c (MD5) 1_63070100005.pdf: 267678 bytes, checksum: 6e117ca7ccce2de984bce0f4cc915746 (MD5) Previous issue date: 2009-08-18T00:00:00Z<br>The term structure interest rate determination is one of th

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33

Thouvenin, Pierre-Antoine. "Modeling spatial and temporal variabilities in hyperspectral image unmixing." Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/19258/1/THOUVENIN_PierreAntoine.pdf.

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Acquired in hundreds of contiguous spectral bands, hyperspectral (HS) images have received an increasing interest due to the significant spectral information they convey about the materials present in a given scene. However, the limited spatial resolution of hyperspectral sensors implies that the observations are mixtures of multiple signatures corresponding to distinct materials. Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing the data -- referred to as endmembers -- and their relative proportion in each pixel according to a predefined mixture model.

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34

Schwarzenegger, Rafael. "Matematické modely spolehlivosti v technické praxi." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-318802.

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Tato práce popisuje a aplikuje parametrické a neparametrické modely spolehlivosti na cenzorovaná data. Ukazuje implementaci spolehlivosti v metodologii Six Sigma. Metody jsou využity pro přežití/spolehlivost reálných technických dat.

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Milcher, Susanne, and Manfred M. Fischer. "On labour market discrimination against Roma in South East Europe." WU Vienna University of Economics and Business, 2010. http://epub.wu.ac.at/3960/1/SSRN%2Did1739103.pdf.

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This paper directs interest on country-specific labour market discrimination Roma may suffer in South East Europe. The study lies in the tradition of statistical Blinder-Oaxaca decomposition analysis. We use microdata from UNDP's 2004 survey of Roma minorities, and apply a Bayesian approach, proposed by Keith and LeSage (2004), for the decomposition analysis of wage differentials. This approach is based on a robust Bayesian heteroscedastic linear regression model in conjunction with Markov Chain Monte Carlo (MCMC) estimation. The results obtained indicate the presence of labour market di

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36

Leininger, Thomas J. "An Adaptive Bayesian Approach to Dose-Response Modeling." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3325.pdf.

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Kastner, Gregor, Sylvia Frühwirth-Schnatter, and Hedibert Freitas Lopes. "Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/4875/1/research_report_updated.pdf.

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We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational and Graphical Statistics, 20(3), 531-570, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almo

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Andersson, Lovisa. "An application of Bayesian Hidden Markov Models to explore traffic flow conditions in an urban area." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385187.

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This study employs Bayesian Hidden Markov Models as method to explore vehicle traffic flow conditions in an urban area in Stockholm, based on sensor data from separate road positions. Inter-arrival times are used as the observed sequences. These sequences of inter-arrival times are assumed to be generated from the distributions of four different (and hidden) traffic flow states; nightly free flow, free flow, mixture and congestion. The filtered and smoothed probability distributions of the hidden states and the most probable state sequences are obtained by using the forward, forward-backward a

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Aslan, Sipan. "Comparison Of Missing Value Imputation Methods For Meteorological Time Series Data." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612426/index.pdf.

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Dealing with missing data in spatio-temporal time series constitutes important branch of general missing data problem. Since the statistical properties of time-dependent data characterized by sequentiality of observations then any interruption of consecutiveness in time series will cause severe problems. In order to make reliable analyses in this case missing data must be handled cautiously without disturbing the series statistical properties, mainly as temporal and spatial dependencies. In this study we aimed to compare several imputation methods for the appropriate completion of missing val

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40

Oulad, Ameziane Mehdi. "Amélioration de l'exploration de l'espace d'état dans les méthodes de Monte Carlo séquentielles pour le suivi visuel." Thesis, Ecole centrale de Lille, 2017. http://www.theses.fr/2017ECLI0007.

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Le suivi visuel constitue une tâche essentielle en vision par ordinateur. Les approches Bayésiennes sont largement utilisées aujourd’hui pour résoudre la problématique du suivi visuel. Notamment grâce aux possibilités offertes par les méthodes de Monte Carlo séquentielles (SMC) qui prennent en comptes les incertitudes du model et s’adaptent à des scenarios variés. L’efficacité des méthodes SMC dépend fortement du choix de la loi de proposition qui permet d’explorer l’espace d’état.Dans cette thèse, nous cherchons à améliorer l’exploration de l’espace d’état en approchant la loi de proposition

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41

Silva, Maria Joseane Cruz da. "Imputação múltipla: comparação e eficiência em experimentos multiambientais." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-08082012-143901/.

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Em experimentos de genótipos ambiente são comuns à presença de valores ausentes, devido à quantidade insuficiente de genótipos para aplicação dificultando, por exemplo, o processo de recomendação de genótipos mais produtivos, pois para a aplicação da maioria das técnicas estatísticas multivariadas exigem uma matriz de dados completa. Desta forma, aplicam-se métodos que estimam os valores ausentes a partir dos dados disponíveis conhecidos como imputação de dados (simples e múltiplas), levando em consideração o padrão e o mecanismo de dados ausentes. O objetivo deste trabalho é avaliar a eficiê

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42

Behlouli, Abdeslam. "Simulation du canal optique sans fil. Application aux télécommunications optique sans fil." Thesis, Poitiers, 2016. http://www.theses.fr/2016POIT2308/document.

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Le contexte de cette thèse est celui des communications optiques sans fil pour des applications en environnements indoor. Pour discuter des performances d'une liaison optique sans fil, il est nécessaire d'établir une étude caractéristique du comportement du canal de propagation. Cette étude passe par l'étape de la mesure ou de l'estimation par la simulation de la réponse impulsionnelle. Après avoir décrit la composition d'une liaison et passé en revue les méthodes de simulation existantes, nous présentons nos algorithmes de simulation dans des environnements réalistes, en nous intéressant à le

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43

Reynolds, Toby J. "Bayesian modelling of integrated data and its application to seabird populations." Thesis, University of St Andrews, 2010. http://hdl.handle.net/10023/1635.

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Integrated data analyses are becoming increasingly popular in studies of wild animal populations where two or more separate sources of data contain information about common parameters. Here we develop an integrated population model using abundance and demographic data from a study of common guillemots (Uria aalge) on the Isle of May, southeast Scotland. A state-space model for the count data is supplemented by three demographic time series (productivity and two mark-recapture-recovery (MRR)), enabling the estimation of prebreeder emigration rate - a parameter for which there is no direct obser

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Calmet, Claire. "Inférences sur l'histoire des populations à partir de leur diversité génétique : étude de séquences démographiques de type fondation-explosion." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2002. http://tel.archives-ouvertes.fr/tel-00288526.

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L'étude de la démographie dans une perspective historique participe à la compréhension des processus évolutifs. Les données de diversité génétique sont potentiellement informatives quant au passé démographique des populations: en effet, ce passé est enregistré avec perte d'information par les marqueurs moléculaires, par l'intermédiaire de leur histoire généalogique et mutationnelle. L'acquisition de données de diversité génétique est de plus en plus rapide et aisée, et concerne potentiellement n'importe quel organisme d'intérêt. D'où un effort dans la dernière décennie pour développer les outi

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Bakra, Eleni. "Aspects of population Markov chain Monte Carlo and reversible jump Markov chain Monte Carlo." Thesis, University of Glasgow, 2009. http://theses.gla.ac.uk/1247/.

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Holenstein, Roman. "Particle Markov chain Monte Carlo." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/7319.

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Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods have emerged as the two main tools to sample from high-dimensional probability distributions. Although asymptotic convergence of MCMC algorithms is ensured under weak assumptions, the performance of these latters is unreliable when the proposal distributions used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. In this thesis we propose a new Monte Carlo framework in which we build efficient high-dimensional proposal distributions using SMC methods. This allows us to

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Byrd, Jonathan Michael Robert. "Parallel Markov Chain Monte Carlo." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/3634/.

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The increasing availability of multi-core and multi-processor architectures provides new opportunities for improving the performance of many computer simulations. Markov Chain Monte Carlo (MCMC) simulations are widely used for approximate counting problems, Bayesian inference and as a means for estimating very highdimensional integrals. As such MCMC has found a wide variety of applications in fields including computational biology and physics,financial econometrics, machine learning and image processing. This thesis presents a number of new method for reducing the runtime of Markov Chain Monte

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Gomes, André Yoshizumi. "Família Weibull de razão de chances na presença de covariáveis." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/4558.

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Made available in DSpace on 2016-06-02T20:06:06Z (GMT). No. of bitstreams: 1 4331.pdf: 1908865 bytes, checksum: d564b46a6111fdca6f7cc9f4d5596637 (MD5) Previous issue date: 2009-03-18<br>Universidade Federal de Minas Gerais<br>The Weibull distribuition is a common initial choice for modeling data with monotone hazard rates. However, such distribution fails to provide a reasonable parametric _t when the hazard function is unimodal or bathtub-shaped. In this context, Cooray (2006) proposed a generalization of the Weibull family by considering the distributions of the odds of Weibull and inverse

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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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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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