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Dissertations / Theses on the topic 'Statistical computation'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Liu, Meng-Chang. "Achieving privacy-preserving distributed statistical computation." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/achieving-privacypreserving-distributed-statistical-computation(6831db5c-d605-4a38-9711-7592d2b94e01).html.

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The growth of the Internet has opened up tremendous opportunities for cooperative computations where the results depend on the private data inputs of distributed participating parties. In most cases, such computations are performed by multiple mutually untrusting parties. This has led the research community into studying methods for performing computation across the Internet securely and efficiently. This thesis investigates security methods in the search for an optimum solution to privacy- preserving distributed statistical computation problems. For this purpose, the nonparametric sign test a

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2

Jiang, Huijing. "Statistical computation and inference for functional data analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37087.

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My doctoral research dissertation focuses on two aspects of functional data analysis (FDA): FDA under spatial interdependence and FDA for multi-level data. The first part of my thesis focuses on developing modeling and inference procedure for functional data under spatial dependence. The methodology introduced in this part is motivated by a research study on inequities in accessibility to financial services. The first research problem in this part is concerned with a novel model-based method for clustering random time functions which are spatially interdependent. A cluster consists of time

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3

Ajayi, Ajibola. "Direct computation of statistical variations in electromagnetic problems." Thesis, University of Nottingham, 2008. http://eprints.nottingham.ac.uk/10539/.

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This work described in this thesis develops a computationally efficient approach to performing electromagnetic simulations in the presence of statistically defined uncertainties caused by either material inhomogeneities, or fabrication and placement tolerances. Comparisons are made with results from Monte Carlo simulations and a sequence of higher order approximation extensions is considered. There are two main techniques used to achieve the overall objective of this thesis namely: the Direct Solution Technique (DST) and the Unscented Transform (UT) method. The DST based on Taylor series appro

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4

Wong, Samuel Wing Kwong. "Statistical Computation for Problems in Dynamic Systems and Protein Folding." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11036.

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Inference for dynamic systems and conformational sampling for protein folding are two problems motivated by applied data, which pose computational challenges from a statistical perspective. Dynamic systems are often described by a set of coupled differential equations, and methods of parametric estimation for these models from noisy data can require repeatedly solving the equations numerically. Many of these models also lead to rough likelihood surfaces, which makes sampling difficult. We introduce a method for Bayesian inference on these models, using a multiple chain framework that exploi

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5

Qvarfordt, Viktor. "Non-Abelian Anyons: Statistical Repulsion and Topological Quantum Computation." Thesis, KTH, Matematik (Avd.), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-207177.

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As opposed to classical mechanics, quantum mechanical particles can be truly identical and lead to new and interesting phenomena. Identical particles can be of different types, determined by their exchange symmetry, which in turn gives rise to statistical repulsion. The exchange symmetry is given by a representation of the exchange group; the fundamental group of the configuration space of identical particles. In three dimensions the exchange group is the permutation group and there are only two types of identical particles; bosons and fermions. While any number of bosons can be at the same pl

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6

Simola, Umberto. "Developments in Approximate Bayesian Computation and Statistical Applications in Astrostatistics." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3423284.

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The title of this Thesis embraces two topics that have been investigated. Most of the present work is dedicated to develops and extensions for Approximate Bayesian Computation (ABC). While several algorithms have been proposed to improve the efficiency of the basic ABC algorithm, a number of subjective choices is left to the researcher. Several of these choices have not only an impact on the efficiency of the algorithm but also on its capability to approximate properly the true posterior distribution. We present a first extension of the ABC Population Monte-Carlo (ABC-PMC) algorithm aimed by t

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7

Adams, Niall. "Parallel processing for statistical computation with particular emphasis on bootstrap methods." Thesis, Liverpool John Moores University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388525.

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8

Jones, Lee K., and Richard C. 1943 Larson. "Efficient Computation of Probabilities of Events Described by Order Statistics and Application to a Problem of Queues." Massachusetts Institute of Technology, Operations Research Center, 1991. http://hdl.handle.net/1721.1/5159.

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Consider a set of N i.i.d. random variables in [0, 1]. When the experimental values of the random variables are arranged in ascending order from smallest to largest, one has the order statistics of the set of random variables. In this note an O(N3) algorithm is developed for computing the probability that the order statistics vector lies in a given rectangle. The new algorithm is then applied to a problem of statistical inference in queues. Illustrative computational results are included.

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9

Lieberman, Chad Eric. "Parameter and state model reduction for Bayesian statistical inverse problems." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54213.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2009.<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 (p. 113-118).<br>Decisions based on single-point estimates of uncertain parameters neglect regions of significant probability. We consider a paradigm based on decision-making under uncertainty including three steps: identification of parametri

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10

Daly, Aidan C. "Statistical tools and community resources for developing trusted models in biology and chemistry." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:4e936e3b-7985-44f0-814c-7be3433bdcbb.

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Mathematical modeling has been instrumental to the development of natural sciences over the last half-century. Through iterated interactions between modeling and real-world exper- imentation, these models have furthered our understanding of the processes in biology and chemistry that they seek to represent. In certain application domains, such as the field of car- diac biology, communities of modelers with common interests have emerged, leading to the development of many models that attempt to explain the same or similar phenomena. As these communities have developed, however, reporting standa

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11

Bauman, G. John. "Computation of Weights for Probabilistic Record Linkage Using the EM Algorithm." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1361.pdf.

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12

Ramdas, Aaditya Kumar. "Computational and Statistical Advances in Testing and Learning." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/790.

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This thesis makes fundamental computational and statistical advances in testing and estimation, making critical progress in theory and application of classical statistical methods like classification, regression and hypothesis testing, and understanding the relationships between them. Our work connects multiple fields in often counter-intuitive and surprising ways, leading to new theory, new algorithms, and new insights, and ultimately to a cross-fertilization of varied fields like optimization, statistics and machine learning. The first of three thrusts has to do with active learning, a form

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13

Panigrahi, Sunil Kumar, Soubhik Chakraborty, and Jibitesh Mishra. "A Statistical Analysis of Bubble Sort in terms of Serial and Parallel Computation." IJCSN Journal, 2012. http://hdl.handle.net/10150/214089.

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In some recent papers, the weight based statistical bounds have arguably explained time complexity better than the count based mathematical bounds. This is definitely true for average case where for an arbitrary code it is difficult to identify the pivotal operation or pivotal region in the code for taking the expectation and/or when the probability distribution, over which expectation is taken, becomes unrealistic over the problem domain. In worst case, it can certify whether a mathematical bound is conservative or not. Here we revisit the results on Bubble sort in sequential mode an

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14

Blocker, Alexander Weaver. "Distributed and Multiphase Inference in Theory and Practice: Principles, Modeling, and Computation for High-Throughput Science." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:10977.

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The rise of high-throughput scientific experimentation and data collection has introduced new classes of statistical and computational challenges. The technologies driving this data explosion are subject to complex new forms of measurement error, requiring sophisticated statistical approaches. Simultaneously, statistical computing must adapt to larger volumes of data and new computational environments, particularly parallel and distributed settings. This dissertation presents several computational and theoretical contributions to these challenges. In chapter 1, we consider the problem of estim

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15

Hallinan, Jennifer Susan. "Detection of malignancy associated changes in cervical cells using statistical and evolutionary computation techniques /." St. Lucia, Qld, 1999. http://adt.library.uq.edu.au/public/adt-QU2000.0037/index.html.

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Bruno, Matteo. "Maximum entropy methods for the statistical analysis of bipartite networks: fast computation and applications." Thesis, IMT Alti Studi Lucca, 2021. http://e-theses.imtlucca.it/338/1/Bruno_phdthesis.pdf.

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Many real systems can be represented as networks, and their study can unveil hidden information and provide an extra insight to our understanding of the systems. However, finding the right model for a system is a challenging and fundamental task, as the use of a biased or approximated model can lead to wrongful conclusions. In this thesis we focus on maximum entropy network models. In particular, we focus on bipartite networks, that are networks in which there are two types of nodes and interactions are allowed only between two nodes of different type. In the first part of the thesis,

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17

Aeschbacher, Simon. "Statistical inference on evolutionary processes in Alpine ibex (Capra ibex) : mutation, migration and selection." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5793.

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The thesis begins with a general introduction to population genetics in chapter 1. I review the fundamental processes of evolution - mutation, recombination, selection, gene flow and genetic drift - and give an overview of Bayesian inference in statistical population genetics. Later, I introduce the studied species, Alpine ibex (Capra ibex ), and its recent history. This history is intimately linked to the structured population in the Swiss Alps that provides the source of genetic data for this thesis. A particular focus is devoted to approximate Bayesian computation (ABC) in chapter 2, a meth

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18

Xie, Zheng. "Computation reduction for statistical analysis of the effect of nano-CMOS variability on integrated circuits." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/computation-reduction-for-statistical-analysis-of-the-effect-of-nanocmos-variability-on-integrated-circuits(43d25ba2-4905-46df-bb58-ab84bffdc2d2).html.

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The intrinsic atomistic variability of nano-scale integrated circuit (IC) technology must be taken into account when analysing circuit designs to predict likely yield. These ‘atomistic’ variabilities are random in nature and are so great that new circuit analysis techniques are needed which adopt a statistical treatment of the variability of device performances. Monte Carlo (MC) based statistical techniques aim to do this by analysing many randomized copies of the circuit. The randomization can take into account correlation between parameters due to both intra-die and inter-die effects. A majo

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19

Raynal, Louis. "Bayesian statistical inference for intractable likelihood models." Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS035/document.

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Dans un processus d’inférence statistique, lorsque le calcul de la fonction de vraisemblance associée aux données observées n’est pas possible, il est nécessaire de recourir à des approximations. C’est un cas que l’on rencontre très fréquemment dans certains champs d’application, notamment pour des modèles de génétique des populations. Face à cette difficulté, nous nous intéressons aux méthodes de calcul bayésien approché (ABC, Approximate Bayesian Computation) qui se basent uniquement sur la simulation de données, qui sont ensuite résumées et comparées aux données observées. Ces comparaisons

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20

Rava, Andrea Basilio. "Quantum approximate optimization algorithm: combinatorial problems and classical statistical models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23113/.

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The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm for solving combinatorial optimization problems. Since most of combinatorial optimization problems may be thought as particular instances of Ising Hamiltonians, the study of the QAOA is very relevant from the physical point of view for its potential applications in describing physical systems. In the QAOA a quantum state is prepared and, through 2p parameterized quantum evolutions, a final state which represents an extreme of cost function and encodes the approximate solution of the problem is obtaine

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21

Chernoff, Parker. "Sabermetrics - Statistical Modeling of Run Creation and Prevention in Baseball." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3663.

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The focus of this thesis was to investigate which baseball metrics are most conducive to run creation and prevention. Stepwise regression and Liu estimation were used to formulate two models for the dependent variables and also used for cross validation. Finally, the predicted values were fed into the Pythagorean Expectation formula to predict a team’s most important goal: winning. Each model fit strongly and collinearity amongst offensive predictors was considered using variance inflation factors. Hits, walks, and home runs allowed, infield putouts, errors, defense-independent earned run aver

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22

Siebert, Julien. "Statistical mechanics of the self-gravitating gases." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2005. http://tel.archives-ouvertes.fr/hal-00009142.

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The self-gravitating systems are formed by particles interacting through gravity. They describe structure formation in the universe. As a consequence of the long range interaction of gravity, they are inhomogeneous even at thermal equilibrium. We study the self-gravitating systems with several kinds of particles and the self-gravitating systems in the presence of the cosmological constant $ Lambda$. We formulate the statistical mechanics and the mean field approach describing the gaseous phase. We explicitely compute the density of particles and thermodynamic quantities. The presence of $ Lamb

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23

Powell, Nathaniel V. "The role of Uncertainty in Categorical Perception Utilizing Statistical Learning in Robots." ScholarWorks @ UVM, 2016. http://scholarworks.uvm.edu/graddis/581.

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At the heart of statistical learning lies the concept of uncertainty. Similarly, embodied agents such as robots and animals must likewise address uncertainty, as sensation is always only a partial reflection of reality. This thesis addresses the role that uncertainty can play in a central building block of intelligence: categorization. Cognitive agents are able to perform tasks like categorical perception through physical interaction (active categorical perception; ACP), or passively at a distance (distal categorical perception; DCP). It is possible that the former scaffolds the learning of th

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24

Xu, Tianbing. "Nonparametric evolutionary clustering." Diss., Online access via UMI:, 2009.

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25

Kent-Dobias, Jaron P. "Energy-Driven Pattern Formation in Planar Dipole-Dipole Systems." Scholarship @ Claremont, 2014. http://scholarship.claremont.edu/hmc_theses/66.

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A variety of two-dimensional fluid systems, known as dipole-mediated systems, exhibit a dipole-dipole interaction between their fluid constituents. The com- petition of this repulsive dipolar force with the cohesive fluid forces cause these systems to form intricate and patterned structures in their boundaries. In this thesis, we show that the microscopic details of any such system are irrelevant in the macroscopic limit and contribute only to a constant offset in the system’s energy. A numeric model is developed, and some important stable domain morphologies are characterized. Previously unre

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26

Lee, Xing Ju. "Statistical and simulation modelling for enhanced understanding of hospital pathogen and related health issues." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/103762/1/Xing%20Ju_Lee_Thesis.pdf.

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This thesis investigated the temporal occurrence and transmission of within hospital pathogens using appropriate statistical and simulation models applied to imperfect hospital data. The research provides new insights into the transmission dynamics of methicillin-resistant Staphylococcus aureus within a hospital ward to assist infection control and prevention efforts. Additionally, appropriate statistical methods are identified to analyse hospital infection data which take into account the intricacies and potential limitations of such data.

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27

Merkle, Edgar C. "Bayesian estimation of factor analysis models with incomplete data." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1126895149.

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Thesis (Ph. D.)--Ohio State University, 2005.<br>Title from first page of PDF file. Document formatted into pages; contains xi, 106 p.; also includes graphics. Includes bibliographical references (p. 103-106). Available online via OhioLINK's ETD Center

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28

Polcer, James. "Generalized Bathtub Hazard Models for Binary-Transformed Climate Data." TopSCHOLAR®, 2011. http://digitalcommons.wku.edu/theses/1060.

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In this study, we use a hazard-based modeling as an alternative statistical framework to time series methods as applied to climate data. Data collected from the Kentucky Mesonet will be used to study the distributional properties of the duration of high and low-energy wind events relative to an arbitrary threshold. Our objectiveswere to fit bathtub models proposed in literature, propose a generalized bathtub model, apply these models to Kentucky Mesonet data, and make recommendations as to feasibility of wind power generation. Using two different thresholds (1.8 and 10 mph respectively), resul

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29

Monson, Christopher Kenneth. "No Free Lunch, Bayesian Inference, and Utility: A Decision-Theoretic Approach to Optimization." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1292.pdf.

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Molaro, Mark Christopher. "Computational statistical methods in chemical engineering." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/111286.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 175-182).<br>Recent advances in theory and practice, have introduced a wide variety of tools from machine learning that can be applied to data intensive chemical engineering problems. This thesis covers applications of statistical learning spanning a range of relative importance of data versus existing detailed theory. In each application, the quantity and quality of data available from experimental systems are u

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31

Zagordi, Osvaldo. "Statistical physics methods in computational biology." Doctoral thesis, SISSA, 2007. http://hdl.handle.net/20.500.11767/3971.

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The interest of statistical physics for combinatorial optimization is not new, it suffices to think of a famous tool as simulated annealing. Recently, it has also resorted to statistical inference to address some "hard" optimization problems, developing a new class of message passing algorithms. Three applications to computational biology are presented in this thesis, namely: 1) Boolean networks, a model for gene regulatory networks; 2) haplotype inference, to study the genetic information present in a population; 3) clustering, a general machine learning tool.

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32

Lafond, Jean. "Matrix completion : statistical and computational aspects." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLT002/document.

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Dans cette thèse nous nous intéressons aux méthodes de complétion de matrices de faible rang et étudions certains problèmes reliés. Un premier ensemble de résultats visent à étendre les garanties statistiques existantes pour les modèles de complétion avec bruit additif sous-gaussiens à des distributions plus générales. Nous considérons en particulier les distributions multinationales et les distributions appartenant à la famille exponentielle. Pour ces dernières, nous prouvons l'optimalité (au sens minimax) à un facteur logarithmique près des estimateurs à pénalité norme trace. Un second ensem

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Lafond, Jean. "Matrix completion : statistical and computational aspects." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLT002.

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Dans cette thèse nous nous intéressons aux méthodes de complétion de matrices de faible rang et étudions certains problèmes reliés. Un premier ensemble de résultats visent à étendre les garanties statistiques existantes pour les modèles de complétion avec bruit additif sous-gaussiens à des distributions plus générales. Nous considérons en particulier les distributions multinationales et les distributions appartenant à la famille exponentielle. Pour ces dernières, nous prouvons l'optimalité (au sens minimax) à un facteur logarithmique près des estimateurs à pénalité norme trace. Un second ensem

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Ozogur-akyuz, Sureyya. "A Mathematical Contribution Of Statistical Learning And Continuous Optimization Using Infinite And Semi-infinite Programming To Computational Statistics." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12610381/index.pdf.

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A subfield of artificial intelligence, machine learning (ML), is concerned with the development of algorithms that allow computers to &ldquo<br>learn&rdquo<br>. ML is the process of training a system with large number of examples, extracting rules and finding patterns in order to make predictions on new data points (examples). The most common machine learning schemes are supervised, semi-supervised, unsupervised and reinforcement learning. These schemes apply to natural language processing, search engines, medical diagnosis, bioinformatics, detecting credit fraud, stock market analysis, classi

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Vines, Susan Karen. "Bayesian computation in epidemiology." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285259.

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Pennec, Xavier. "Statistical Computing on Manifolds for Computational Anatomy." Habilitation à diriger des recherches, Université de Nice Sophia-Antipolis, 2006. http://tel.archives-ouvertes.fr/tel-00633163.

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During the last decade, my main research topic was on medical image analysis, and more particularly on image registration. However, I was also following in background a more theoretical research track on statistical computing on manifolds. With the recent emergence of computational anatomy, this topic gained a lot of importance in the medical image analysis community. During the writing of this habilitation manuscript, I felt that it was time to present a more simple and uni ed view of how it works and why it can be important. This is why the usual short synthesis of the habilitation became a

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Ma, Xiaojuan. "Computational and statistical aspects of pricing models." Thesis, University of Nottingham, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.606378.

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This thesis is motivated by the problem of modelling and analysing real share price data. Various approaches and models are considered. One approach is to consider a random walk on a discrete-time Markov chain perturbed by Gaussian noise as a model for real share price data. To implement this model a numerical algorithm is constructed to treat the NP hard Emdedding problem. A second approach to modelling share price data is to consider a random walk on the lamplighter group perturbed by Gaussian noise. This class of problems leads to interesting theoretical questions about asymptotic behaviour

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Han, Ningren. "Computational and statistical approaches to optical spectroscopy." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120432.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 223-236).<br>Compact and smart optical sensors have had a major impact on people's lives over the last decade. Although the spatial information provided by optical imaging systems has already had a major impact, there is untapped potential in the spectroscopic domain. By transforming molecular information into wavelength-domain data, optical spectroscopy techniques have become some of the m

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Deng, Xinwei. "Contributions to statistical learning and statistical quantification in nanomaterials." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29777.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Wu, C. F. Jeff; Committee Co-Chair: Yuan, Ming; Committee Member: Huo, Xiaoming; Committee Member: Vengazhiyil, Roshan Joseph; Committee Member: Wang, Zhonglin. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Saul, Lawrence K. (Lawrence Kevin) 1969. "Exact computations in the statistical mechanics of disordered systems." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/28063.

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Moffa, Giuseppina. "Some computational advances in sampling based statistical inference." Thesis, University of Bristol, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535203.

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Kuceyeski, Amy Frances. "Efficient Computational and Statistical Models of Hepatic Metabolism." Cleveland, Ohio : Case Western Reserve University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1238694223.

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Sohn, Michael B. "Novel Computational and Statistical Approaches in Metagenomic Studies." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556866.

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Metagenomics has a great potential to discover previously unattainable information about microbial communities. The simplest, but extremely powerful approach for studying the characteristics of a microbial community is the analysis of differential abundance, which tries to identify differentially abundant features (e.g. species or genes) across different communities. For instance, detection of differentially abundant microbes across healthy and diseased groups can enable us to identify potential pathogens or probiotics. However, the analysis of differential abundance could mislead us about the

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Fletcher, W. A. J. "Computational statistics in molecular phylogenetics." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1306705/.

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Simulation remains a very important approach to testing the robustness and accuracy of phylogenetic inference methods. However, current simulation programs are limited, especially concerning realistic models for simulating insertions and deletions (indels). In this thesis I implement a new, portable and flexible application, named INDELible, which can be used to generate nucleotide, amino acid and codon sequence data by simulating indels (under several models of indel length distribution) as well as substitutions (under a rich repertoire of substitution models). In particular, I introduce a si

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South, Leah Frances. "Contributions to computational Bayesian statistics." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132155/1/Leah_South_Thesis.pdf.

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Current statistical methods for facilitating data-driven decision making are too computationally intensive when the phenomenon of interest is complex. This thesis develops novel statistical algorithms and software that can be used to obtain timely, reliable insights when the underlying physical system is complex. The new methods will allow practitioners in areas such as ecology, biology, finance and astronomy to gain more understanding of their field and to make better-informed decisions.

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Harlow, Jennifer. "Data-Adaptive Multivariate Density Estimation Using Regular Pavings, With Applications to Simulation-Intensive Inference." Thesis, University of Canterbury. School of Mathematics and Statistics, 2013. http://hdl.handle.net/10092/9160.

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A regular paving (RP) is a finite succession of bisections that partitions a multidimensional box into sub-boxes using a binary tree-based data structure, with the restriction that an existing sub-box in the partition may only be bisected on its first widest side. Mapping a real value to each element of the partition gives a real-mapped regular paving (RMRP) that can be used to represent a piecewise-constant function density estimate on a multidimensional domain. The RP structure allows real arithmetic to be extended to density estimates represented as RMRPs. Other operations such as computi

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Garcia-Puente, Luis David. "Algebraic Geometry of Bayesian Networks." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11133.

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We develop the necessary theory in algebraic geometry to place Bayesian networks into the realm of algebraic statistics. This allows us to create an algebraic geometry--statistics dictionary. In particular, we study the algebraic varieties defined by the conditional independence statements of Bayesian networks. A complete algebraic classification, in terms of primary decomposition of polynomial ideals, is given for Bayesian networks on at most five random variables. Hidden variables are related to the geometry of higher secant varieties. Moreover, a complete algebraic classification, in ter

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48

Chermaneanu, Raducu. "Représentation de la variabilité des propriétés mécaniques d’un CMO à l’échelle microscopique : Méthodes de construction des distributions statistiques." Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14485/document.

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Aujourd’hui, les matériaux composites sont très largement utilisés, notamment dans la réalisation de structures aéronautiques, grâce à leurs nombreux avantages fonctionnels. Leurs caractéristiques mécaniques spécifiques (propriétés/masse volumique) nettement supérieures à d’autres matériaux plus classiques, tels que l’acier ou l’aluminium et la réalisation de formes complexes, font de ces matériaux des candidats très compétitifs dans de nombreux secteurs au-delà de l’aéronautique. Toutefois, ces matériaux présentent à différentes échelles d’observation des sources de variabilité caractéristiqu

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49

Clark, Stephen. "Class-based statistical models for lexical knowledge acquisition." Thesis, University of Sussex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341541.

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This thesis is about the automatic acquisition of a particular kind of lexical knowledge, namely the knowledge of which noun senses can fill the argument slots of predicates. The knowledge is represented using probabilities, which agrees with the intuition that there are no absolute constraints on the arguments of predicates, but that the constraints are satisfied to a certain degree; thus the problem of knowledge acquisition becomes the problem of probability estimation from corpus data. The problem with defining a probability model in terms of senses is that this involves a huge number of pa

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50

Stein, Nathan Mathes. "Advances in Empirical Bayes Modeling and Bayesian Computation." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11051.

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Chapter 1 of this thesis focuses on accelerating perfect sampling algorithms for a Bayesian hierarchical model. A discrete data augmentation scheme together with two different parameterizations yields two Gibbs samplers for sampling from the posterior distribution of the hyperparameters of the Dirichlet-multinomial hierarchical model under a default prior distribution. The finite-state space nature of this data augmentation permits us to construct two perfect samplers using bounding chains that take advantage of monotonicity and anti-monotonicity in the target posterior distribution, but both

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