Relevant bibliographies by topics / Variational bayesian inference / Dissertations / Theses
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Dissertations / Theses on the topic 'Variational bayesian inference'

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Published: 4 June 2021
Last updated: 27 July 2025

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1

Beal, Matthew James. "Variational algorithms for approximate Bayesian inference." Thesis, University College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404387.

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2

Maestrini, Luca. "On variational approximations for frequentist and bayesian inference." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3424936.

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Variational approximations are approximate inference techniques for complex statisticalmodels providing fast, deterministic alternatives to conventional methods that,however accurate, take much longer to run. We extend recent work concerning variationalapproximations developing and assessing some variational tools for likelihoodbased and Bayesian inference. In particular, the first part of this thesis employs a Gaussian variational approximation strategy to handle frequentist generalized linear mixedmodels with general design random effects matrices such as those including spline basisfunctio
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3

Houghton, Adrian James. "Variational Bayesian inference for comparison Var(1) models." Thesis, University of Newcastle Upon Tyne, 2009. http://hdl.handle.net/10443/790.

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Suppose that we wish to determine which models in a candidate set are most likely to have given rise to a set of observed data. Then, it is well-established that, from a Bayesian viewpoint, evaluation of the marginal likelihood for each candidate is a crucial step to this end. For the purposes of model comparison, this will enable subsequent computation of both Bayes’ factors and posterior model probabilities. Given its evident significance in this area, it is thus regrettable that analytic calculation of the marginal likelihood is often not possible. To tackle this problem, one recent additio
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4

Teng, Jing. "Variational filtering for bayesian inference in wireless sensor networks." Troyes, 2009. http://www.theses.fr/2009TROY0019.

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Dans cette thèse, nous traitons les problèmes d'inférence bayésienne décentralisée dans les réseaux de capteurs sans fil (RCSF). Une approche variationnelle est proposée dans cette thèse afin de s’accommoder des contraintes énergétiques et des contraintes de transmission, inhérentes dans le cadre des RCSF. Trois applications, étroitement liées, ont été traitées: le tracking d'une seule cible, le tracking de plusieurs cibles et l’auto-localisation et le suivi de cible simultanés (SLAT). L’implémentation décentralisée de l’approche variationnelle repose sur un compromis entre la précision de l’e
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5

Matthews, Alexander Graeme de Garis. "Scalable Gaussian process inference using variational methods." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/278022.

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Gaussian processes can be used as priors on functions. The need for a flexible, principled, probabilistic model of functional relations is common in practice. Consequently, such an approach is demonstrably useful in a large variety of applications. Two challenges of Gaussian process modelling are often encountered. These are dealing with the adverse scaling with the number of data points and the lack of closed form posteriors when the likelihood is non-Gaussian. In this thesis, we study variational inference as a framework for meeting these challenges. An introductory chapter motivates the use
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Abeywardana, Sachinthaka. "Variational Inference in Generalised Hyperbolic and von Mises-Fisher Distributions." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/16504.

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Most real world data are skewed, contain more than the set of real numbers, and have higher probabilities of extreme events occurring compared to a normal distribution. In this thesis we explore two non-Gaussian distributions, the Generalised Hyperbolic Distribution (GHD) and, the von-Mises Fisher (vMF) Distribution. These distributions are studied in the context of 1) Regression in heavy tailed data, 2) Quantifying variance of functions with reference to finding relevant quantiles and, 3) Clustering data that lie on the surface of the sphere. Firstly, we extend Gaussian Processes (GPs) and
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7

Ocone, Andrea. "Variational inference for Gaussian-jump processes with application in gene regulation." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8280.

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In the last decades, the explosion of data from quantitative techniques has revolutionised our understanding of biological processes. In this scenario, advanced statistical methods and algorithms are becoming fundamental to decipher the dynamics of biochemical mechanisms such those involved in the regulation of gene expression. Here we develop mechanistic models and approximate inference techniques to reverse engineer the dynamics of gene regulation, from mRNA and/or protein time series data. We start from an existent variational framework for statistical inference in transcriptional networks.
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8

Steinberg, John. "A Comparative Analysis of Bayesian Nonparametric Variational Inference Algorithms for Speech Recognition." Master's thesis, Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/216605.

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Electrical and Computer Engineering<br>M.S.E.E.<br>Nonparametric Bayesian models have become increasingly popular in speech recognition tasks such as language and acoustic modeling due to their ability to discover underlying structure in an iterative manner. These methods do not require a priori assumptions about the structure of the data, such as the number of mixture components, and can learn this structure directly. Dirichlet process mixtures (DPMs) are a widely used nonparametric Bayesian method which can be used as priors to determine an optimal number of mixture components and their resp
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9

Huix, Tom. "Variational Inference : theory and large scale applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX071.

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Cette thèse développe des méthodes d'Inférence Variationnelle pour l'apprentissage bayésien en grande dimension. L'approche bayésienne en machine learning permet de gérer l'incertitude épistémique des modèles et ainsi de mieux quantifier l'incertitude de ces modèles, ce qui est nécessaire dans de nombreuses applications de machine learning. Cependant, l'inférence bayésienne n'est souvent pas réalisable car la distribution à posteriori des paramètres du modèle n'est pas calculable en général. L'Inférence Variationnelle (VI) est une approche qui permet de contourner ce problème en approximant la
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10

Marklund, Emil. "Bayesian inference in aggregated hidden Markov models." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243090.

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Single molecule experiments study the kinetics of molecular biological systems. Many such studies generate data that can be described by aggregated hidden Markov models, whereby there is a need of doing inference on such data and models. In this study, model selection in aggregated Hidden Markov models was performed with a criterion of maximum Bayesian evidence. Variational Bayes inference was seen to underestimate the evidence for aggregated model fits. Estimation of the evidence integral by brute force Monte Carlo integration theoretically always converges to the correct value, but it conver
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11

Nissilä, M. (Mauri). "Iterative receivers for digital communications via variational inference and estimation." Doctoral thesis, University of Oulu, 2008. http://urn.fi/urn:isbn:9789514286865.

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Abstract In this thesis, iterative detection and estimation algorithms for digital communications systems in the presence of parametric uncertainty are explored and further developed. In particular, variational methods, which have been extensively applied in other research fields such as artificial intelligence and machine learning, are introduced and systematically used in deriving approximations to the optimal receivers in various channel conditions. The key idea behind the variational methods is to transform the problem of interest into an optimization problem via an introduction of extra d
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12

OLOBATUYI, KEHINDE IBUKUN. "A Family of Variational Algorithms for Approximate Bayesian Inference of High-Dimensional Data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/325856.

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L’approccio Bayesiano alle tecniche di machine-learning consente di integrare in un modello le informazioni a priori per evitare problemi di overfitting, cercando di approssimare la distribuzione a posteriori. Fornisce inoltre una metodologia coerente per la scelta fra diversi modelli alternativi, e richiede tipicamente uno sforzo computazionale considerevole, tale da rendere alcuni problemi intrattabili. Questa tesi propone una famiglia di metodologie di tipo Variational Bayes per approssimare la complessità computazionale dell’approccio Bayesiano tramite l’utilizzo di variabili latenti, min
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13

Wang, Jiabin. "Variational Bayes inference based segmentation algorithms for brain PET-CT images." Thesis, The University of Sydney, 2012. https://hdl.handle.net/2123/29251.

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Dual modality PET-CT imaging can provide aligned anatomical (CT) and functional (PET) images in a single scanning session, and has nowadays steadily replaced single modality PET imaging in clinical practice. The enormous number of PET-CT images produced in hospitals are currently analysed almost entirely through visual inspection on a slice-by-slice basis, which requires a high degree of skill and concentration, and is time-consuming, expensive, prone to operator bias, and unsuitable for the processing large-scale studies. Computer-aided diagnosis, where image segmentation is an essential step
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Grullon, Dylan Emanuel Centeno. "Disentangling time constant and time dependent hidden state in time series with variational Bayesian inference." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/124572.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 85-86).<br>In this thesis, we design and explore a new model architecture called a Variational Bayes Recurrent Neural Network (VBRNN) for modelling time series. The VBRNN contains explicit structure to disentangle time constan
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Lienart, Thibaut. "Inference on Markov random fields : methods and applications." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:3095b14c-98fb-4bda-affc-a1fa1708f628.

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This thesis considers the problem of performing inference on undirected graphical models with continuous state spaces. These models represent conditional independence structures that can appear in the context of Bayesian Machine Learning. In the thesis, we focus on computational methods and applications. The aim of the thesis is to demonstrate that the factorisation structure corresponding to the conditional independence structure present in high-dimensional models can be exploited to decrease the computational complexity of inference algorithms. First, we consider the smoothing problem on Hid
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16

Burchett, Woodrow. "Improving the Computational Efficiency in Bayesian Fitting of Cormack-Jolly-Seber Models with Individual, Continuous, Time-Varying Covariates." UKnowledge, 2017. http://uknowledge.uky.edu/statistics_etds/27.

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The extension of the CJS model to include individual, continuous, time-varying covariates relies on the estimation of covariate values on occasions on which individuals were not captured. Fitting this model in a Bayesian framework typically involves the implementation of a Markov chain Monte Carlo (MCMC) algorithm, such as a Gibbs sampler, to sample from the posterior distribution. For large data sets with many missing covariate values that must be estimated, this creates a computational issue, as each iteration of the MCMC algorithm requires sampling from the full conditional distributions of
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17

Back, Alexander, and William Keith. "Bayesian Neural Networks for Financial Asset Forecasting." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252562.

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Neural networks are powerful tools for modelling complex non-linear mappings, but they often suffer from overfitting and provide no measures of uncertainty in their predictions. Bayesian techniques are proposed as a remedy to these problems, as these both regularize and provide an inherent measure of uncertainty from their posterior predictive distributions. By quantifying predictive uncertainty, we attempt to improve a systematic trading strategy by scaling positions with uncertainty. Exact Bayesian inference is often impossible, and approximate techniques must be used. For this task, this th
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18

Cheema, Prasad. "Machine Learning for Inverse Structural-Dynamical Problems: From Bayesian Non-Parametrics, to Variational Inference, and Chaos Surrogates." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/24139.

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To ensure that the design of a structure is both robust and efficient, engineers often investigate inverse dynamical modeling problems. In particular, there are three archetypal inverse modeling problems which arise in the context of structural engineering. These are respectively: (i) The eigenvalue assignment problem, (ii) Bayesian model updating, and (iii) Operational modal analysis. It is the intent of this dissertation to investigate all three aforementioned inverse dynamical problems within the broader context of modern machine learning advancements. Firstly, the inverse eigenvalue assi
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19

Huo, Shuning. "Bayesian Modeling of Complex High-Dimensional Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/101037.

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With the rapid development of modern high-throughput technologies, scientists can now collect high-dimensional complex data in different forms, such as medical images, genomics measurements. However, acquisition of more data does not automatically lead to better knowledge discovery. One needs efficient and reliable analytical tools to extract useful information from complex datasets. The main objective of this dissertation is to develop innovative Bayesian methodologies to enable effective and efficient knowledge discovery from complex high-dimensional data. It contains two parts—the developme
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20

Lauretig, Adam M. "Natural Language Processing, Statistical Inference, and American Foreign Policy." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1562147711514566.

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Michelen, Strofer Carlos Alejandro. "Machine Learning and Field Inversion approaches to Data-Driven Turbulence Modeling." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103155.

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There still is a practical need for improved closure models for the Reynolds-averaged Navier-Stokes (RANS) equations. This dissertation explores two different approaches for using experimental data to provide improved closure for the Reynolds stress tensor field. The first approach uses machine learning to learn a general closure model from data. A novel framework is developed to train deep neural networks using experimental velocity and pressure measurements. The sensitivity of the RANS equations to the Reynolds stress, required for gradient-based training, is obtained by means of both variat
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22

Wenzel, Florian. "Scalable Inference in Latent Gaussian Process Models." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/20926.

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Latente Gauß-Prozess-Modelle (latent Gaussian process models) werden von Wissenschaftlern benutzt, um verborgenen Muster in Daten zu er- kennen, Expertenwissen in probabilistische Modelle einfließen zu lassen und um Vorhersagen über die Zukunft zu treffen. Diese Modelle wurden erfolgreich in vielen Gebieten wie Robotik, Geologie, Genetik und Medizin angewendet. Gauß-Prozesse definieren Verteilungen über Funktionen und können als flexible Bausteine verwendet werden, um aussagekräftige probabilistische Modelle zu entwickeln. Dabei ist die größte Herausforderung, eine geeignete Inferenzmethode zu
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23

Nguyen, Trong Nghia. "Deep Learning Based Statistical Models for Business and Financial Data." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26944.

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We investigate a wide range of statistical models commonly used in many business and financial econometrics applications and propose flexible ways to combine these highly interpretable models with powerful predictive models in the deep learning literature to leverage the advantages and compensate the disadvantages of each of the modelling approaches. Our approaches of utilizing deep learning techniques for financial data are different from the recently proposed deep learning-based models in the financial econometrics literature in several perspectives. First, we do not overlook well-establishe
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Das, Debasish. "Bayesian Sparse Regression with Application to Data-driven Understanding of Climate." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/313587.

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Computer and Information Science<br>Ph.D.<br>Sparse regressions based on constraining the L1-norm of the coefficients became popular due to their ability to handle high dimensional data unlike the regular regressions which suffer from overfitting and model identifiability issues especially when sample size is small. They are often the method of choice in many fields of science and engineering for simultaneously selecting covariates and fitting parsimonious linear models that are better generalizable and easily interpretable. However, significant challenges may be posed by the need to accommoda
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Zhang, Fan. "Statistical Methods for Characterizing Genomic Heterogeneity in Mixed Samples." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/419.

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"Recently, sequencing technologies have generated massive and heterogeneous data sets. However, interpretation of these data sets is a major barrier to understand genomic heterogeneity in complex diseases. In this dissertation, we develop a Bayesian statistical method for single nucleotide level analysis and a global optimization method for gene expression level analysis to characterize genomic heterogeneity in mixed samples. The detection of rare single nucleotide variants (SNVs) is important for understanding genetic heterogeneity using next-generation sequencing (NGS) data. Various compu
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McClure, Patrick. "Adapting deep neural networks as models of human visual perception." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/278073.

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Deep neural networks (DNNs) have recently been used to solve complex perceptual and decision tasks. In particular, convolutional neural networks (CNN) have been extremely successful for visual perception. In addition to performing well on the trained object recognition task, these CNNs also model brain data throughout the visual hierarchy better than previous models. However, these DNNs are still far from completely explaining visual perception in the human brain. In this thesis, we investigated two methods with the goal of improving DNNs’ capabilities to model human visual perception: (1) dee
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Cohen, Max. "Metamodel and bayesian approaches for dynamic systems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS003.

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Dans ce manuscrit, nous développons des architectures d'apprentissage profond pour modéliser la consommation énergétique et la qualité de l'air de bâtiments.Nous présentons d'abord une méthodologie de bout-en-bout permettant d'optimiser la demande énergétique tout en améliorant le confort, en substituant au traditionnel simulateur physique un modèle num'eriquement plus efficace.A partir de données historiques, nous vérifions que les simulations de ce métamodèle correspondent aux conditions réelles du bâtiment.Cependant, les performances des prédictions sont dégradées dans certaines situations
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Rouillard, Louis. "Bridging Simulation-based Inference and Hierarchical Modeling : Applications in Neuroscience." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG024.

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La neuroimagerie étudie l'architecture et le fonctionnement du cerveau à l'aide de la résonance magnétique (IRM). Pour comprendre le signal complexe observé, les neuroscientifiques émettent des hypothèses sous la forme de modèles explicatifs, régis par des paramètres interprétables. Cette thèse étudie l'inférence statistique : deviner quels paramètres auraient pu produire le signal à travers le modèle.L'inférence en neuroimagerie est complexifiée par au moins trois obstacles : une grande dimensionnalité, une grande incertitude et la structure hiérarchique des données. Pour s'attaquer à ce régi
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Rossi, Simone. "Improving Scalability and Inference in Probabilistic Deep Models." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS042.

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Au cours de la dernière décennie, l'apprentissage profond a atteint un niveau de maturité suffisant pour devenir le choix privilégié pour résoudre les problèmes liés à l'apprentissage automatique ou pour aider les processus de prise de décision.En même temps, l'apprentissage profond n'a généralement pas la capacité de quantifier avec précision l'incertitude de ses prédictions, ce qui rend ces modèles moins adaptés aux applications critiques en matière de risque.Une solution possible pour résoudre ce problème est d'utiliser une formulation bayésienne ; cependant, bien que cette solution soit él
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Christmas, Jacqueline. "Robust spatio-temporal latent variable models." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3051.

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Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are widely-used mathematical models for decomposing multivariate data. They capture spatial relationships between variables, but ignore any temporal relationships that might exist between observations. Probabilistic PCA (PPCA) and Probabilistic CCA (ProbCCA) are versions of these two models that explain the statistical properties of the observed variables as linear mixtures of an alternative, hypothetical set of hidden, or latent, variables and explicitly model noise. Both the noise and the latent variables are assumed
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Simpson, Edwin Daniel. "Combined decision making with multiple agents." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:f5c9770b-a1c9-4872-b0dc-1bfa28c11a7f.

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In a wide range of applications, decisions must be made by combining information from multiple agents with varying levels of trust and expertise. For example, citizen science involves large numbers of human volunteers with differing skills, while disaster management requires aggregating information from multiple people and devices to make timely decisions. This thesis introduces efficient and scalable Bayesian inference for decision combination, allowing us to fuse the responses of multiple agents in large, real-world problems and account for the agents’ unreliability in a principled manner. A
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Simpson, Ivor James Alexander. "A probabilistic approach to non-rigid medical image registration." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:7824e67a-5403-48b1-8b54-cb714eef5055.

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Non-rigid image registration is an important tool for analysing morphometric differences in subjects with Alzheimer's disease from structural magnetic resonance images of the brain. This thesis describes a novel probabilistic approach to non-rigid registration of medical images, and explores the benefits of its use in this area of neuroimaging. Many image registration approaches have been developed for neuroimaging. The vast majority suffer from two limitations: Firstly, the trade-off between image fidelity and regularisation requires selection. Secondly, only a point-estimate of the mapping b
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El, Haj Abir. "Stochastics blockmodels, classifications and applications." Thesis, Poitiers, 2019. http://www.theses.fr/2019POIT2300.

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Cette thèse de doctorat porte sur l’analyse de réseaux pondérés, graphes finis où chaque arête est associée à un poids représentant l’intensité de sa force. Nous introduisons une extension du modèle à blocs stochastiques (SBM) binaire, appelée modèle à blocs stochastiques binomial (bSBM). Cette question est motivée par l’étude des réseaux de co-citations dans un contexte de fouille de textes où les données sont représentées par un graphe. Les noeuds sont des mots et chaque arête joignant deux mots est pondérée par le nombre de documents inclus dans le corpus citant simultanément cette paire de
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Torossian, Léonard. "Méthodes d'apprentissage statistique pour la régression et l'optimisation globale de mesures de risque." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30192.

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Cette thèse s'inscrit dans le contexte général de l'estimation et de l'optimisation de fonctions de type boîte noire dont la sortie est une variable aléatoire. Motivé par la nécessité de quantifier l'occurrence d'événements extrêmes dans des disciplines comme la médecine, l'agriculture ou la finance, dans cette thèse des indicateurs sur certaines propriétés de la distribution en sortie, comme la variance ou la taille des queues de dis- tribution, sont étudiés. De nombreux indicateurs, aussi connus sous le nom de mesure de risque, ont été proposés dans la littérature ces dernières années. Dans
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Jaureguiberry, Xabier. "Fusion pour la séparation de sources audio." Thesis, Paris, ENST, 2015. http://www.theses.fr/2015ENST0030/document.

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La séparation aveugle de sources audio dans le cas sous-déterminé est un problème mathématique complexe dont il est aujourd'hui possible d'obtenir une solution satisfaisante, à condition de sélectionner la méthode la plus adaptée au problème posé et de savoir paramétrer celle-ci soigneusement. Afin d'automatiser cette étape de sélection déterminante, nous proposons dans cette thèse de recourir au principe de fusion. L'idée est simple : il s'agit, pour un problème donné, de sélectionner plusieurs méthodes de résolution plutôt qu'une seule et de les combiner afin d'en améliorer la solution. Pour
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Bakhous, Christine. "Modèles d'encodage parcimonieux de l'activité cérébrale mesurée par IRM fonctionnelle." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00933426.

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L'imagerie par résonance magnétique fonctionnelle (IRMf) est une technique non invasive permettant l'étude de l'activité cérébrale au travers des changements hémodynamiques associés. Récemment, une technique de détection-estimation conjointe (DEC) a été développée permettant d'alterner (1) la détection de l'activité cérébrale induite par une stimulation ainsi que (2) l'estimation de la fonction de réponse hémodynamique caractérisant la dynamique vasculaire; deux problèmes qui sont généralement traités indépendamment. Cette approche considère une parcellisation a priori du cerveau en zones fonc
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Jaureguiberry, Xabier. "Fusion pour la séparation de sources audio." Electronic Thesis or Diss., Paris, ENST, 2015. http://www.theses.fr/2015ENST0030.

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La séparation aveugle de sources audio dans le cas sous-déterminé est un problème mathématique complexe dont il est aujourd'hui possible d'obtenir une solution satisfaisante, à condition de sélectionner la méthode la plus adaptée au problème posé et de savoir paramétrer celle-ci soigneusement. Afin d'automatiser cette étape de sélection déterminante, nous proposons dans cette thèse de recourir au principe de fusion. L'idée est simple : il s'agit, pour un problème donné, de sélectionner plusieurs méthodes de résolution plutôt qu'une seule et de les combiner afin d'en améliorer la solution. Pour
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Rio, Maxime. "Modèles bayésiens pour la détection de synchronisations au sein de signaux électro-corticaux." Phd thesis, Université de Lorraine, 2013. http://tel.archives-ouvertes.fr/tel-00859307.

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Cette thèse propose de nouvelles méthodes d'analyse d'enregistrements cérébraux intra-crâniens (potentiels de champs locaux), qui pallie les lacunes de la méthode temps-fréquence standard d'analyse des perturbations spectrales événementielles : le calcul d'une moyenne sur les enregistrements et l'emploi de l'activité dans la période pré-stimulus. La première méthode proposée repose sur la détection de sous-ensembles d'électrodes dont l'activité présente des synchronisations cooccurrentes en un même point du plan temps-fréquence, à l'aide de modèles bayésiens de mélange gaussiens. Les sous-ense
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Alari, Anna. "Variations temporelles et géographiques des méningites à pneumocoque et effet du vaccin conjugué en France." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLV070/document.

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Streptococcus pneumoniae est une bactérie cocci gram positif commensale de la flore oropharyngée qui colonise le rhinopharynx de l’Homme et dont près de 100 sérotypes sont connus. Les nourrissons et les jeunes enfants représentent son réservoir principal. Le pneumocoque peut être à l’origine d’infections graves, telles que la méningite, les bactériémies et la pneumonie, et moins graves mais plus courantes comme la sinusite et l’otite moyenne aiguë. Deux vaccins anti-pneumococciques conjugués ont été introduits en France : le PCV7 (couvrant contre 7 sérotypes) en 2003 et le PCV13 (couvrant cont
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Chen, Liang. "Small population bias and sampling effects in stochastic mortality modelling." Thesis, Heriot-Watt University, 2017. http://hdl.handle.net/10399/3372.

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Pension schemes are facing more difficulties on matching their underlying liabilities with assets, mainly due to faster mortality improvements for their underlying populations, better environments and medical treatments and historically low interest rates. Given most of the pension schemes are relatively much smaller than the national population, modelling and forecasting the small populations' longevity risk become urgent tasks for both the industrial practitioners and academic researchers. This thesis starts with a systematic analysis on the influence of population size on the uncertainties
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Rio, Maxime. "Modèles bayésiens pour la détection de synchronisations au sein de signaux électro-corticaux." Electronic Thesis or Diss., Université de Lorraine, 2013. http://www.theses.fr/2013LORR0090.

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Cette thèse propose de nouvelles méthodes d'analyse d'enregistrements cérébraux intra-crâniens (potentiels de champs locaux), qui pallie les lacunes de la méthode temps-fréquence standard d'analyse des perturbations spectrales événementielles : le calcul d'une moyenne sur les enregistrements et l'emploi de l'activité dans la période pré-stimulus. La première méthode proposée repose sur la détection de sous-ensembles d'électrodes dont l'activité présente des synchronisations cooccurrentes en un même point du plan temps-fréquence, à l'aide de modèles bayésiens de mélange gaussiens. Les sous-ense
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Tsai, Yu-Chun, and 蔡鈺群. "Variational Bayesian Inference Nonnegative Matrix Factorization with Application to Auditory Streaming." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/64067406417230739039.

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碩士<br>國立交通大學<br>工學院聲音與音樂創意科技碩士學位學程<br>102<br>In the application of audio streaming or so called audio source separation, the goal is to decompose a music recording into sound streams from individual instruments. One of the most effective classes of methods to separate sound streams stems from the nonnegative matrix factorization (NMF). This thesis presents a variational Bayesian (VB) treatment of NMF, based on the Itakura-Saito (IS) divergence and the concepts of hyper-parameters, and derives the marginal likelihood (low bound) to approximate the posterior density of the NMF factors. An effici
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Waters, Austin Severn. "Infinite-word topic models for digital media." Thesis, 2014. http://hdl.handle.net/2152/24968.

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Digital media collections hold an unprecedented source of knowledge and data about the world. Yet, even at current scales, the data exceeds by many orders of magnitude the amount a single user could browse through in an entire lifetime. Making use of such data requires computational tools that can index, search over, and organize media documents in ways that are meaningful to human users, based on the meaning of their content. This dissertation develops an automated approach to analyzing digital media content based on topic models. Its primary contribution, the Infinite-Word Topic Model (I
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(10702392), Alana K. Lund. "Bayesian Identification of Nonlinear Structural Systems: Innovations to Address Practical Uncertainty." Thesis, 2021.

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The ability to rapidly assess the condition of a structure in a manner which enables the accurate prediction of its remaining capacity has long been viewed as a crucial step in allowing communities to make safe and efficient use of their public infrastructure. This objective has become even more relevant in recent years as both the interdependency and state of deterioration in infrastructure systems throughout the world have increased. Current practice for structural condition assessment emphasizes visual inspection, in which trained professionals will routinely survey a structure to estimate
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Han, Shaobo. "Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas." Diss., 2016. http://hdl.handle.net/10161/12828.

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<p>Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used facto
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Otto, Dominik. "Computational Gene Expression Deconvolution." 2020. https://ul.qucosa.de/id/qucosa%3A75763.

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Technologies such as micro-expression arrays and high-throughput sequenc- ing assays have accelerated research of genetic transcription in biological cells. Furthermore, many links between the gene expression levels and the pheno- typic characteristics of cells have been discovered. Our current understanding of transcriptomics as an intermediate regulatory layer between genomics and proteomics raises hope that we will soon be able to decipher many more cel- lular mechanisms through the exploration of gene transcription. However, although large amounts of expression data are measured, only lim-
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