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Tesi sul tema "Monte Carlo sampling and estimation"

Autore: Grafiati
Pubblicato: 21 dicembre 2024

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1

Chen, Wen-shiang. "Bayesian estimation by sequential Monte Carlo sampling for nonlinear dynamic systems." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1086146309.

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Thesis (Ph. D.)--Ohio State University, 2004.<br>Title from first page of PDF file. Document formatted into pages; contains xiv, 117 p. : ill. (some col.). Advisors: Bhavik R. Bakshi and Prem K. Goel, Department of Chemical Engineering. Includes bibliographical references (p. 114-117).
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2

Gilabert, Navarro Joan Francesc. "Estimation of binding free energies with Monte Carlo atomistic simulations and enhanced sampling." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/669282.

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The advances in computing power have motivated the hope that computational methods can accelerate the pace of drug discovery pipelines. For this, fast, reliable and user-friendly tools are required. One of the fields that has gotten more attentions is the prediction of binding affinities. Two main problems have been identified for such methods: insufficient sampling and inaccurate models. This thesis is focused on tackling the first problem. To this end, we present the development of efficient methods for the estimation of protein-ligand binding free energies. We have developed a protocol tha
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3

Xu, Nuo. "A Monte Carlo Study of Single Imputation in Survey Sampling." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-30541.

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Missing values in sample survey can lead to biased estimation if not treated. Imputation was posted asa popular way to deal with missing values. In this paper, based on Särndal (1994, 2005)’s research, aMonte-Carlo simulation is conducted to study how the estimators work in different situations and howdifferent imputation methods work for different response distributions.
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4

Ptáček, Martin. "Spatial Function Estimation with Uncertain Sensor Locations." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-449288.

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Tato práce se zabývá úlohou odhadování prostorové funkce z hlediska regrese pomocí Gaussovských procesů (GPR) za současné nejistoty tréninkových pozic (pozic senzorů). Nejdříve je zde popsána teorie v pozadí GPR metody pracující se známými tréninkovými pozicemi. Tato teorie je poté aplikována při odvození výrazů prediktivní distribuce GPR v testovací pozici při uvážení nejistoty tréninkových pozic. Kvůli absenci analytického řešení těchto výrazů byly výrazy aproximovány pomocí metody Monte Carlo. U odvozené metody bylo demonstrováno zlepšení kvality odhadu prostorové funkce oproti standardnímu
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5

Diop, Khadim. "Estimation de la fiabilité d'un palier fluide." Thesis, Angers, 2015. http://www.theses.fr/2015ANGE0029/document.

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Les travaux de recherche constituent une contribution au développement de la théorie de la fiabilité en mécanique des fluides. Pour la conception de machines et de systèmes mécatroniques complexes, de nombreux composants fluides, difficiles à dimensionner, sont utilisés. Ces derniers ont des caractéristiques intrinsèques statiques et dynamiques sensibles et ont donc une grande importance sur la fiabilité et la durée de vie de la plupart des machines et des systèmes.Le développement effectué se concentre spécialement sur l'évaluation de la fiabilité d’un palier fluide grâce à un couplage « méca
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6

Greer, Brandi A. Young Dean M. "Bayesian and pseudo-likelihood interval estimation for comparing two Poisson rate parameters using under-reported data." Waco, Tex. : Baylor University, 2008. http://hdl.handle.net/2104/5283.

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7

Thajeel, Jawad. "Kriging-based Approaches for the Probabilistic Analysis of Strip Footings Resting on Spatially Varying Soils." Thesis, Nantes, 2017. http://www.theses.fr/2017NANT4111/document.

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L’analyse probabiliste des ouvrages géotechniques est généralement réalisée en utilisant la méthode de simulation de Monte Carlo. Cette méthode n’est pas adaptée pour le calcul des faibles probabilités de rupture rencontrées dans la pratique car elle devient très coûteuse dans ces cas en raison du grand nombre de simulations requises pour obtenir la probabilité de rupture. Dans cette thèse, nous avons développé trois méthodes probabilistes (appelées AK-MCS, AK-IS et AK-SS) basées sur une méthode d’apprentissage (Active learning) et combinant la technique de Krigeage et l’une des trois méthodes
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8

Wu, Yi-Fang. "Accuracy and variability of item parameter estimates from marginal maximum a posteriori estimation and Bayesian inference via Gibbs samplers." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/5879.

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Item response theory (IRT) uses a family of statistical models for estimating stable characteristics of items and examinees and defining how these characteristics interact in describing item and test performance. With a focus on the three-parameter logistic IRT (Birnbaum, 1968; Lord, 1980) model, the current study examines the accuracy and variability of the item parameter estimates from the marginal maximum a posteriori estimation via an expectation-maximization algorithm (MMAP/EM) and the Markov chain Monte Carlo Gibbs sampling (MCMC/GS) approach. In the study, the various factors which have
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9

Argouarc, h. Elouan. "Contributions to posterior learning for likelihood-free Bayesian inference." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS021.

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L'inférence bayésienne a posteriori est utilisée dans de nombreuses applications scientifiques et constitue une méthodologie répandue pour la prise de décision en situation d'incertitude. Elle permet aux praticiens de confronter les observations du monde réel à des modèles d'observation pertinents, et d'inférer en retour la distribution d'une variable explicative. Dans de nombreux domaines et applications pratiques, nous considérons des modèles d'observation complexes pour leur pertinence scientifique, mais au prix de densités de probabilité incalculables. En conséquence, à la fois la vraisemb
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10

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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11

Chéhab, L'Émir Omar. "Advances in Self-Supervised Learning : applications to neuroscience and sample-efficiency." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG079.

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L'apprentissage auto-supervisé a gagné en popularité en tant que méthode d'apprentissage à partir de données non annotées. Il s'agit essentiellement de créer puis de résoudre un problème de prédiction qui utilise les données; par exemple, de retrouver l'ordre de données qui ont été mélangées. Ces dernières années, cette approche a été utilisée avec succès pour entraîner des réseaux de neurones qui extraient des représentations utiles des données, le tout sans aucune annotation. Cependant, notre compréhension de ce qui est appris et de la qualité de cet apprentissage est limitée. Ce document éc
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12

Vo, Brenda. "Novel likelihood-free Bayesian parameter estimation methods for stochastic models of collective cell spreading." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/99588/1/Brenda_Vo_Thesis.pdf.

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Biological processes underlying skin cancer growth and wound healing are governed by various collective cell spreading mechanisms. This thesis develops new statistical methods to provide key insights into the mechanisms driving the spread of cell populations such as motility, proliferation and cell-to-cell adhesion, using experimental data. The new methods allow us to precisely estimate the parameters of such mechanisms, quantify the associated uncertainty and investigate how these mechanisms are influenced by various factors. The thesis provides a useful tool to measure the efficacy of medica
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13

Sebastian, Shalin. "Empirical evaluation of Monte Carlo sampling /." Available to subscribers only, 2005. http://proquest.umi.com/pqdweb?did=1075709431&sid=9&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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14

Ekwegh, Ijeoma W. "Newsvendor Models With Monte Carlo Sampling." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etd/3125.

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Newsvendor Models with Monte Carlo Sampling by Ijeoma Winifred Ekwegh The newsvendor model is used in solving inventory problems in which demand is random. In this thesis, we will focus on a method of using Monte Carlo sampling to estimate the order quantity that will either maximizes revenue or minimizes cost given that demand is uncertain. Given data, the Monte Carlo approach will be used in sampling data over scenarios and also estimating the probability density function. A bootstrapping process yields an empirical distribution for the order quantity that will maximize the expected profit. F
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15

Karagulyan, Avetik. "Sampling with the Langevin Monte-Carlo." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAG002.

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L’échantillonnage des lois aléatoires est un problème de taille en statistique et en machine learning. Les approches générales sur ce sujet sont souvent divisées en deux catégories: fréquentiste vs bayésienne. L’approche fréquentiste corresponds à la minimisation du risque empirique, c’est à dire à l’estimation du maximum vraisemblance qui est un problème d’optimisation, tandis que l’approche bayésienne revient à intégrer la loi postérieure. Cette dernière approche nécessite souvent des méthodes approximatives car l’intégrale n’est généralement pas tractable. Dans ce manuscrit, nous allons étu
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16

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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17

Han, Xiao-liang. "Markov Chain Monte Carlo and sampling efficiency." Thesis, University of Bristol, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333974.

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18

Nilmeier, Jerome P. "Monte Carlo methods for sampling protein configurations." Diss., Search in ProQuest Dissertations & Theses. UC Only, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3324625.

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19

Ahmed, Ilyas. "Importance Sampling for Least-Square Monte Carlo Methods." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193080.

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Pricing American style options is challenging due to early exercise opportunities. The conditional expectation in the Snell envelope, known as the continuation value is approximated by basis functions in the Least-Square Monte Carlo-algorithm, giving robust estimation for the options price. By change of measure in the underlying Geometric Brownain motion using Importance Sampling, the variance of the option price can be reduced up to 9 times. Finding the optimal estimator that gives the minimal variance requires careful consideration on the reference price without adding bias in the estimator.
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20

Boualem, Abdelbassit. "Estimation de distribution de tailles de particules par techniques d'inférence bayésienne." Thesis, Orléans, 2016. http://www.theses.fr/2016ORLE2030/document.

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Ce travail de recherche traite le problème inverse d’estimation de la distribution de tailles de particules (DTP) à partir des données de la diffusion dynamique de lumière (DLS). Les méthodes actuelles d’estimation souffrent de la mauvaise répétabilité des résultats d’estimation et de la faible capacité à séparer les composantes d’un échantillon multimodal de particules. L’objectif de cette thèse est de développer de nouvelles méthodes plus performantes basées sur les techniques d’inférence bayésienne et cela en exploitant la diversité angulaire des données de la DLS. Nous avons proposé tout d
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21

Schäfer, Christian. "Monte Carlo methods for sampling high-dimensional binary vectors." Phd thesis, Université Paris Dauphine - Paris IX, 2012. http://tel.archives-ouvertes.fr/tel-00767163.

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This thesis is concerned with Monte Carlo methods for sampling high-dimensional binary vectors from complex distributions of interest. If the state space is too large for exhaustive enumeration, these methods provide a mean of estimating the expected value with respect to some function of interest. Standard approaches are mostly based on random walk type Markov chain Monte Carlo, where the equilibrium distribution of the chain is the distribution of interest and its ergodic mean converges to the expected value. We propose a novel sampling algorithm based on sequential Monte Carlo methodology w
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22

Giacomuzzi, Genny <1984&gt. "Local Earthquake Tomography by trans-dimensional Monte Carlo Sampling." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5160/1/giacomuzzi_genny_tesi.pdf.

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In this study a new, fully non-linear, approach to Local Earthquake Tomography is presented. Local Earthquakes Tomography (LET) is a non-linear inversion problem that allows the joint determination of earthquakes parameters and velocity structure from arrival times of waves generated by local sources. Since the early developments of seismic tomography several inversion methods have been developed to solve this problem in a linearized way. In the framework of Monte Carlo sampling, we developed a new code based on the Reversible Jump Markov Chain Monte Carlo sampling method (Rj-McMc). It is a
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23

Giacomuzzi, Genny <1984&gt. "Local Earthquake Tomography by trans-dimensional Monte Carlo Sampling." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5160/.

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In this study a new, fully non-linear, approach to Local Earthquake Tomography is presented. Local Earthquakes Tomography (LET) is a non-linear inversion problem that allows the joint determination of earthquakes parameters and velocity structure from arrival times of waves generated by local sources. Since the early developments of seismic tomography several inversion methods have been developed to solve this problem in a linearized way. In the framework of Monte Carlo sampling, we developed a new code based on the Reversible Jump Markov Chain Monte Carlo sampling method (Rj-McMc). It is a
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24

Vadrevu, Aditya M. "Random sampling estimates of fourier transforms antithetical stratified Monte Carlo /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p1450161.

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Thesis (M.S.)--University of California, San Diego, 2008.<br>Title from first page of PDF file (viewed Mar. 27, 2008). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 33-34).
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25

Hörmann, Wolfgang, and Josef Leydold. "Importance Sampling to Accelerate the Convergence of Quasi-Monte Carlo." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/284/1/document.pdf.

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Importance sampling is a well known variance reduction technique for Monte Carlo simulation. For quasi-Monte Carlo integration with low discrepancy sequences it was neglected in the literature although it is easy to see that it can reduce the variation of the integrand for many important integration problems. For lattice rules importance sampling is of highest importance as it can be used to obtain a smooth periodic integrand. Thus the convergence of the integration procedure is accelerated. This can clearly speed up QMC algorithms for integration problems up to dimensions 10 to 12. (author's
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26

Anderson, Luke (Luke James). "An embedded domain specific sampling language for Monte Carlo rendering." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111909.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 95-96).<br>Implementing Monte Carlo integration requires significant domain expertise. While simple algorithms, such as unidirectional path tracing, are relatively forgiving, more complex algorithms, such as bidirectional path tracing or Metropolis methods, are notoriously difficult to implement correctly. We propose a domain specific language for Monte Carlo rendering that offers primitives
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27

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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28

Arnold, Andrea. "Sequential Monte Carlo Parameter Estimation for Differential Equations." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1396617699.

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29

Motula, Paulo Fernando Nericke. "Estimation of DSGE Models: A Monte Carlo Analysis." reponame:Repositório Institucional do FGV, 2013. http://hdl.handle.net/10438/10961.

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Submitted by Paulo Fernando Nericke Motula (pnericke@fgvmail.br) on 2013-06-29T15:45:20Z No. of bitstreams: 1 Dissertacao - Paulo Motula.pdf: 1492951 bytes, checksum: d60fce8c6165733b9666076aef7e2a75 (MD5)<br>Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2013-07-03T13:29:49Z (GMT) No. of bitstreams: 1 Dissertacao - Paulo Motula.pdf: 1492951 bytes, checksum: d60fce8c6165733b9666076aef7e2a75 (MD5)<br>Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2013-07-09T19:35:20Z (GMT) No. of bitstreams: 1 Dissertacao - Paulo Motula.pd
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Hörmann, Wolfgang, and Josef Leydold. "Quasi Importance 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/1394/1/document.pdf.

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There arise two problems when the expectation of some function with respect to a nonuniform multivariate distribution has to be computed by (quasi-) Monte Carlo integration: the integrand can have singularities when the domain of the distribution is unbounded and it can be very expensive or even impossible to sample points from a general multivariate distribution. We show that importance sampling is a simple method to overcome both problems. (author's abstract)<br>Series: Preprint Series / Department of Applied Statistics and Data Processing
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31

VIEIRA, WILSON J. "A General study of undersampling problems in Monte Carlo calculations." reponame:Repositório Institucional do IPEN, 1989. http://repositorio.ipen.br:8080/xmlui/handle/123456789/10258.

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Made available in DSpace on 2014-10-09T12:36:37Z (GMT). No. of bitstreams: 0<br>Made available in DSpace on 2014-10-09T13:59:11Z (GMT). No. of bitstreams: 1 03975.pdf: 1920852 bytes, checksum: 4a74905dfb7a4bb657984043110cfa4f (MD5)<br>Tese (Doutoramento)<br>IPEN/T<br>University of Tennessee, Knoxville, USA
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Karawatzki, Roman, and Josef Leydold. "Automatic Markov Chain Monte Carlo Procedures for Sampling from Multivariate Distributions." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/294/1/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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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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Abstract (sommario):
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

Pierre-Louis, Péguy. "Algorithmic Developments in Monte Carlo Sampling-Based Methods for Stochastic Programming." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/228433.

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Monte Carlo sampling-based methods are frequently used in stochastic programming when exact solution is not possible. In this dissertation, we develop two sets of Monte Carlo sampling-based algorithms to solve classes of two-stage stochastic programs. These algorithms follow a sequential framework such that a candidate solution is generated and evaluated at each step. If the solution is of desired quality, then the algorithm stops and outputs the candidate solution along with an approximate (1 - α) confidence interval on its optimality gap. The first set of algorithms proposed, which we refer
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35

Singh, Gurprit. "Sampling and Variance Analysis for Monte Carlo Integration in Spherical Domain." Thesis, Lyon 1, 2015. http://www.theses.fr/2015LYO10121/document.

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Cette thèse introduit un cadre théorique pour l'étude de différents schémas d'échantillonnage dans un domaine sphérique, et de leurs effets sur le calcul d'intégrales pour l'illumination globale. Le calcul de l'illumination (du transport lumineux) est un composant majeur de la synthèse d'images réalistes, qui se traduit par l'évaluation d'intégrales multidimensionnelles. Les schémas d'intégration numériques de type Monte-Carlo sont utilisés intensivement pour le calcul de telles intégrales. L'un des aspects majeurs de tout schéma d'intégration numérique est l'échantillonnage. En effet, la faço
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36

Janati, el idrissi Yazid. "Monte Carlo Methods for Machine Learning : Practical and Theoretical Contributions for Importance Sampling and Sequential Methods." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS008.

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Cette thèse contribue au vaste domaine des méthodes de Monte Carlo avec de nouveaux algorithmes visant à traiter l'inférence en grande dimension et la quantification de l'incertitude. Dans une première partie, nous développons deux nouvelles méthodes pour l'échantillonnage d'importance. Le premier algorithme est une nouvelle loi de proposition, basée sur sur des étapes d'optimisation et de coût de calcul faible, pour le calcul des constantes de normalisation. L'algorithme résultant est ensuite étendu en un nouvel algorithme MCMC. Le deuxième algorithme est un nouveau schéma pour l'apprentissag
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Quartetti, Douglas A. "A Monte Carlo assessment of estimation in utility analysis." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/29371.

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38

Song, Chenxiao. "Monte Carlo Variance Reduction Methods with Applications in Structural Reliability Analysis." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29801.

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Monte Carlo variance reduction methods have attracted significant interest due to the continuous demand for reducing computational costs in various fields of application. This thesis is based on the content of a collection of six papers contributing to the theory and application of Monte Carlo methods and variance reduction techniques. For theoretical developments, we establish a novel framework of Monte Carlo integration over simplices, throughout from sampling to variance reduction. We also investigate the effect of batching for adaptive variance reduction, which aims at running the Monte Ca
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39

Hörmann, Wolfgang. "New Importance Sampling Densities." Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1066/1/document.pdf.

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To compute the expectation of a function with respect to a multivariate distribution naive Monte Carlo is often not feasible. In such cases importance sampling leads to better estimates than the rejection method. A new importance sampling distribution, the product of one-dimensional table mountain distributions with exponential tails, turns out to be flexible and useful for Bayesian integration problems. To obtain a heavy-tailed importance sampling distribution a new radius transform for the above distribution is suggested. Together with a linear transform the new importance sampling distribut
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40

Zhang, Dali. "Stochastic equilibrium problems with equilibrium constraints, Monte Carlo sampling method and applications." Thesis, University of Southampton, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509541.

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41

Ustun, Berk (Tevfik Berk). "The Markov chain Monte Carlo approach to importance sampling in stochastic programming." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/85220.

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Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2012.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 85-87).<br>Stochastic programming models are large-scale optimization problems that are used to facilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current
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42

Hazelton, Martin Luke. "Method of density estimation with application to Monte Carlo methods." Thesis, University of Oxford, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334850.

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43

Wong, Mei Ning. "Quasi-Monte Carlo sampling for computing the trace of a function of a matrix." HKBU Institutional Repository, 2002. http://repository.hkbu.edu.hk/etd_ra/436.

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44

Bentley, Jason Phillip. "Exact Markov chain Monte Carlo and Bayesian linear regression." Thesis, University of Canterbury. Mathematics and Statistics, 2009. http://hdl.handle.net/10092/2534.

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In this work we investigate the use of perfect sampling methods within the context of Bayesian linear regression. We focus on inference problems related to the marginal posterior model probabilities. Model averaged inference for the response and Bayesian variable selection are considered. Perfect sampling is an alternate form of Markov chain Monte Carlo that generates exact sample points from the posterior of interest. This approach removes the need for burn-in assessment faced by traditional MCMC methods. For model averaged inference, we find the monotone Gibbs coupling from the past (CFTP) a
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45

Cline, David. "Sampling Methods in Ray-Based Global Illumination." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2056.pdf.

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46

Hudson-Curtis, Buffy L. "Generalizations of the Multivariate Logistic Distribution with Applications to Monte Carlo Importance Sampling." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20011101-224634.

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Abstract (sommario):
<p>Monte Carlo importance sampling is a useful numerical integration technique, particularly in Bayesian analysis. A successful importance sampler will mimic the behavior of the posterior distribution, not only in the center, where most of the mass lies, but also in the tails (Geweke, 1989). Typically, the Hessian of the importance sampler is set equal to the Hessian of the posterior distribution evaluated at the mode. Since the importance sampling estimates are weighted averages, their accuracy is assessed by assuming a normal limiting distribution. However, if this scaling of the Hessian lea
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47

Hu, Xinran. "On Grouped Observation Level Interaction and a Big Data Monte Carlo Sampling Algorithm." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/51224.

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Big Data is transforming the way we live. From medical care to social networks, data is playing a central role in various applications. As the volume and dimensionality of datasets keeps growing, designing effective data analytics algorithms emerges as an important research topic in statistics. In this dissertation, I will summarize our research on two data analytics algorithms: a visual analytics algorithm named Grouped Observation Level Interaction with Multidimensional Scaling and a big data Monte Carlo sampling algorithm named Batched Permutation Sampler. These two algorithms are designed
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48

Gilquin, Laurent. "Échantillonnages Monte Carlo et quasi-Monte Carlo pour l'estimation des indices de Sobol' : application à un modèle transport-urbanisme." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM042/document.

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Le développement et l'utilisation de modèles intégrés transport-urbanisme sont devenus une norme pour représenter les interactions entre l'usage des sols et le transport de biens et d'individus sur un territoire. Ces modèles sont souvent utilisés comme outils d'aide à la décision pour des politiques de planification urbaine.Les modèles transport-urbanisme, et plus généralement les modèles mathématiques, sont pour la majorité conçus à partir de codes numériques complexes. Ces codes impliquent très souvent des paramètres dont l'incertitude est peu connue et peut potentiellement avoir un impact i
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49

Kuhlenschmidt, Bernd. "On the stability of sequential Monte Carlo methods for parameter estimation." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709098.

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50

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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