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Tesis sobre el tema "Bayesian non-Parametric model"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

Bartcus, Marius. "Bayesian non-parametric parsimonious mixtures for model-based clustering." Thesis, Toulon, 2015. http://www.theses.fr/2015TOUL0010/document.

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Cette thèse porte sur l’apprentissage statistique et l’analyse de données multi-dimensionnelles. Elle se focalise particulièrement sur l’apprentissage non supervisé de modèles génératifs pour la classiﬁcation automatique. Nous étudions les modèles de mélanges Gaussians, aussi bien dans le contexte d’estimation par maximum de vraisemblance via l’algorithme EM, que dans le contexte Bayésien d’estimation par Maximum A Posteriori via des techniques d’échantillonnage par Monte Carlo. Nous considérons principalement les modèles de mélange parcimonieux qui reposent sur une décomposition spectrale de

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2

Ren, Yan. "A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337085531.

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3

Gebremeskel, Haftu Gebrehiwot. "Implementing hierarchical bayesian model to fertility data: the case of Ethiopia." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424458.

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Background: Ethiopia is a country with 9 ethnically-based administrative regions and 2 city administrations, often cited, among other things, with high fertility rates and rapid population growth rate. Despite the country’s effort in their reduction, they still remain high, especially at regional-level. To this end, the study of fertility in Ethiopia, particularly on its regions, where fertility variation and its repercussion are at boiling point, is paramount important. An easy way of finding different characteristics of a fertility distribution is to build a suitable model of fertility patte

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4

Bratières, Sébastien. "Non-parametric Bayesian models for structured output prediction." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274973.

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Structured output prediction is a machine learning tasks in which an input object is not just assigned a single class, as in classification, but multiple, interdependent labels. This means that the presence or value of a given label affects the other labels, for instance in text labelling problems, where output labels are applied to each word, and their interdependencies must be modelled. Non-parametric Bayesian (NPB) techniques are probabilistic modelling techniques which have the interesting property of allowing model capacity to grow, in a controllable way, with data complexity, while maint

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5

Zhang, Jufen. "Bayesian density estimation and classification of incomplete data using semi-parametric and non parametric models." Thesis, University of Exeter, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426082.

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6

Xu, Yangyi. "Frequentist-Bayesian Hybrid Tests in Semi-parametric and Non-parametric Models with Low/High-Dimensional Covariate." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/71285.

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We provide a Frequentist-Bayesian hybrid test statistic in this dissertation for two testing problems. The first one is to design a test for the significant differences between non-parametric functions and the second one is to design a test allowing any departure of predictors of high dimensional X from constant. The implementation is also given in construction of the proposal test statistics for both problems. For the first testing problem, we consider the statistical difference among massive outcomes or signals to be of interest in many diverse fields including neurophysiology, imaging, e

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7

Knowles, David Arthur. "Bayesian non-parametric models and inference for sparse and hierarchical latent structure." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610403.

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8

Hadrich, Ben Arab Atizez. "Étude des fonctions B-splines pour la fusion d'images segmentées par approche bayésienne." Thesis, Littoral, 2015. http://www.theses.fr/2015DUNK0385/document.

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Dans cette thèse nous avons traité le problème de l'estimation non paramétrique des lois de probabilités. Dans un premier temps, nous avons supposé que la densité inconnue f a été approchée par un mélange de base B-spline quadratique. Puis, nous avons proposé un nouvel estimateur de la densité inconnue f basé sur les fonctions B-splines quadratiques, avec deux méthodes d'estimation. La première est base sur la méthode du maximum de vraisemblance et la deuxième est basée sur la méthode d'estimation Bayésienne MAP. Ensuite, nous avons généralisé notre étude d'estimation dans le cadre du mélange

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9

Yang, Sikun [Verfasser], Heinz Akademischer Betreuer] Köppl, and Kristian [Akademischer Betreuer] [Kersting. "Non-parametric Bayesian Latent Factor Models for Network Reconstruction / Sikun Yang ; Heinz Köppl, Kristian Kersting." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://nbn-resolving.de/urn:nbn:de:tuda-tuprints-96957.

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10

Yang, Sikun [Verfasser], Heinz [Akademischer Betreuer] Köppl, and Kristian [Akademischer Betreuer] Kersting. "Non-parametric Bayesian Latent Factor Models for Network Reconstruction / Sikun Yang ; Heinz Köppl, Kristian Kersting." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://d-nb.info/1204200769/34.

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11

Wei, Wei. "Probabilistic Models of Topics and Social Events." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/941.

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Structured probabilistic inference has shown to be useful in modeling complex latent structures of data. One successful way in which this technique has been applied is in the discovery of latent topical structures of text data, which is usually referred to as topic modeling. With the recent popularity of mobile devices and social networking, we can now easily acquire text data attached to meta information, such as geo-spatial coordinates and time stamps. This metadata can provide rich and accurate information that is helpful in answering many research questions related to spatial and temporal

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12

Okabe, Shu. "Modèles faiblement supervisés pour la documentation automatique des langues." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG091.

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Face à la menace d'extinction de la moitié des langues parlées aujourd'hui d'ici la fin du siècle, la documentation des langues est un domaine de la linguistique notamment consacré à la collecte, annotation et archivage de données. Dans ce contexte, la documentation automatique des langues vise à outiller les linguistes pour faciliter différentes étapes de la documentation, à travers des approches de traitement automatique du langage.Dans le cadre du projet de documentation automatique CLD2025, cette thèse s'intéresse principalement à deux tâches : la segmentation en mots, identifiant les fron

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13

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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14

Rodrigues, Agatha Sacramento. "Estatística em confiabilidade de sistemas: uma abordagem Bayesiana paramétrica." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-10102018-232055/.

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A confiabilidade de um sistema de componentes depende da confiabilidade de cada componente. Assim, a estimação da função de confiabilidade de cada componente do sistema é de interesse. No entanto, esta não é uma tarefa fácil, pois quando o sistema falha, o tempo de falha de um dado componente pode não ser observado, isto é, um problema de dados censurados. Neste trabalho, propomos modelos Bayesianos paramétricos para estimação das funções de confiabilidade de componentes e sistemas em quatro diferentes cenários. Inicialmente, um modelo Weibull é proposto para estimar a distribuição do tempo de

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15

Tran, Gia-Lac. "Advances in Deep Gaussian Processes : calibration and sparsification." Electronic Thesis or Diss., Sorbonne université, 2020. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2020SORUS410.pdf.

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L'intégration des Convolutional Neural Networks (CNNs) et des GPs est une solution prometteuse pour améliorer le pouvoir de représentation des méthodes contemporaines. Dans notre première étude, nous utilisons des diagrammes de fiabilité pour montrer que les combinaisons actuelles de cnns et GPs sont mal calibrées, ce qui donne lieu à des prédictions trop confiantes. En utilisant des Random Feature et la technique d'inférence variationnelle, nous proposons une nouvelle solution correctement calibrée pour combinaisons des CNNs et des GPs. Nous proposons également une extension intuitive de cett

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16

Forti, Marco. "Dynamic Factor Models. Improvements and applications." Doctoral thesis, 2022. http://hdl.handle.net/11573/1613357.

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High-dimensional financial data are characterised by panels of heterogeneous time series, in order to deal with such a complex panels I adopted infinite dimensional Dynamic Factor Models (DFM) to extract volatilities and Bayesian non-parametrics techniques to estimate the parameters of a Stochastic Volatility model. The non-parametric estimation is realised ad an infinite mixture of normals and the combination of such specification with DFM seems to be an original element of this work. The applied exercises worked imply the use of S&P500 daily data spanning over 12 years, the approach ret

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17

FORTI, MARCO. "Dynamic factor models: improvements and applications." Doctoral thesis, 2021. http://hdl.handle.net/11573/1482196.

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In the last two decades data collection, aided by an increased computational capability, has considerably increased both dimension and structure of the datasets; given this, statisticians and economists may today work with time series of remarkable dimension which may come from different sources. Dealing with such datasets may not be so easy and requires the development of ad hoc mathematical models. Dynamic Factor Models (DFM) represent one of the newest techniques in big data management. The adoption of those models allowed me to deepen the study of volatility while introducing Bayesian non

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18

Du, Lan. "Non-parametric bayesian methods for structured topic models." Phd thesis, 2011. http://hdl.handle.net/1885/149800.

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The proliferation of large electronic document archives requires new techniques for automatically analysing large collections, which has posed several new and interesting research challenges. Topic modelling, as a promising statistical technique, has gained significant momentum in recent years in information retrieval, sentiment analysis, images processing, etc. Besides existing topic models, the field of topic modelling still needs to be further explored using more powerful tools. One potentially useful area is to directly consider the document structure ranging from semantically high-level s

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19

Yang, Sikun. "Non-parametric Bayesian Latent Factor Models for Network Reconstruction." Phd thesis, 2020. https://tuprints.ulb.tu-darmstadt.de/9695/13/2019-12-11_YANG_SIKUN.pdf.

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This thesis is concerned with the statistical learning of probabilistic models for graph-structured data. It addresses both the theoretical aspects of network modelling--like the learning of appropriate representations for networks--and the practical difficulties in developing the algorithms to perform inference for the proposed models. The first part of the thesis addresses the problem of discrete-time dynamic network modeling. The objective is to learn the common structure and the underlying interaction dynamics among the entities involved in the observed temporal network. Two probabilist

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20

Martínez, Vargas Danae Mirel. "Régression de Cox avec partitions latentes issues du modèle de Potts." Thèse, 2019. http://hdl.handle.net/1866/22552.

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