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Dissertations / Theses on the topic 'Latent variables model'

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Published: 4 June 2021

Last updated: 8 February 2022

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1

Katsikatsou, Myrsini. "Composite Likelihood Estimation for Latent Variable Models with Ordinal and Continuous, or Ranking Variables." Doctoral thesis, Uppsala universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-188342.

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The estimation of latent variable models with ordinal and continuous, or ranking variables is the research focus of this thesis. The existing estimation methods are discussed and a composite likelihood approach is developed. The main advantages of the new method are its low computational complexity which remains unchanged regardless of the model size, and that it yields an asymptotically unbiased, consistent, and normally distributed estimator. The thesis consists of four papers. The first one investigates the two main formulations of the unrestricted Thurstonian model for ranking data along w

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Strader, Scott C. "A causal model examining AIDS prejudice : AIDS attitudes and homophobia as latent variables." Virtual Press, 1994. http://liblink.bsu.edu/uhtbin/catkey/917830.

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Prejudice and discrimination against people infected with Acquired Immune Deficiency Syndrome (AIDS) is widespread. A significant body of research has examined what personality and demographic characteristics appear to be related to discriminatory behavior, in an attempt to suggest who might be more likely to express prejudicial attitudes and discrimination against people with AIDS. This study tested two causal models which attempted to verify existing theories explaining the influences of demographic and attitudinal factors on the evaluation of a person with AIDS. Specifically, these models s

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Plassmann, Vandana Shah. "Ethnicity and Clothing Expenditures of U.S. Households: A Structural Equations Model with Latent Quality Variables." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/29221.

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The main objective of this study was to determine the relationship between household characteristics and the expenditure shares allocated among various categories of women's clothing for U.S. households belonging to different ethnic groups. The study also estimated unobserved latent quality variables based on household characteristics, and examined the effects of the latent quality variables on the expenditure shares for the various apparel categories. A Multiple Indicator-Multiple Cause Model, which is a special case of the general Structural Equations Model, was used to estimate separate Eng

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Dean, Nema. "Variable selection and other extensions of the mixture model clustering framework /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8943.

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Phonphitakchai, Thanawat. "Modelling the usage rate of a DRT service : a discrete choice model with latent variables." Thesis, University of Aberdeen, 2011. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=165208.

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Demand Responsive Transport (DRT) is a relatively new form of public transport provision; it is an intermediate form somewhere between conventional bus and taxi services. Over the last decade, DRT services have grown in popularity mainly influenced by the development of transport telematics. The telematics-based DRT system, which forms the focus of this research, allows new generation DRT services to have greater flexibility in time and route design, and to enable immediate advance booking and response to travel requests. These DRT services have shown important advantages and benefits in sever

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Almeida, José Pedro Abreu. "The fitting of the German yield curve: a dynamic approach using latent, macroeconomic and stock market variables." Master's thesis, NSBE - UNL, 2010. http://hdl.handle.net/10362/10351.

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics<br>The purpose of this Work Project is to build a yield curve model for the German Government yield curve containing latent variables (Level, Slope and Curvature), macroeconomic variables (German IFO and Inflation Rate) and a stock market variable (German Stock Index DAX), while studying the yield curve dynamics. The model incorporates the Nelson and Siegel (1987) factor model under a State-Space framework and the estimation results provided a goo

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Koch, Rainer, Ulrich Julius, Werner Jaross, and Hans-Egbert Schröder. "Estimation of the Heritability of Latent Variables Which Are Included in a Structural Model for Metabolic Syndrome." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-137470.

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In a study looking for risk factors of atherosclerosis in families with combined hyperlipidemia and hypertension, clinical and biochemical data of 1,149 persons were analyzed to develop two hypothetical multivariate scores concerning the degree to which a patient is affected by the metabolic syndrome. The scores are based on a structural model for low-density cholesterol (LDL) and high-density cholesterol (HDL), triglycerides, uric acid, creatinine, glucose, insulin, systolic blood pressure and waist-to-hip ratio. Age, gender and body mass index were used for adjusting all variables. In segreg

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Koch, Rainer, Ulrich Julius, Werner Jaross, and Hans-Egbert Schröder. "Estimation of the Heritability of Latent Variables Which Are Included in a Structural Model for Metabolic Syndrome." Karger, 2001. https://tud.qucosa.de/id/qucosa%3A27735.

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In a study looking for risk factors of atherosclerosis in families with combined hyperlipidemia and hypertension, clinical and biochemical data of 1,149 persons were analyzed to develop two hypothetical multivariate scores concerning the degree to which a patient is affected by the metabolic syndrome. The scores are based on a structural model for low-density cholesterol (LDL) and high-density cholesterol (HDL), triglycerides, uric acid, creatinine, glucose, insulin, systolic blood pressure and waist-to-hip ratio. Age, gender and body mass index were used for adjusting all variables. In segreg

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Villegas, Arriola Claudia Miluska Maria Luisa, and Bazán Marjorie Daysi Huamán. "Satisfacción e inseguridad laboral como variables explicativas de la intención de renunciar al trabajo en jóvenes." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/652967.

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El presente estudio tiene como objetivo determinar si las variables satisfacción e inseguridad laboral explican la intención de renunciar al trabajo en jóvenes. Se evaluó a 150 jóvenes de edades entre 20 a 35 años que trabajan en una empresa del rubro de telecomunicaciones. Se aplicó la Escala de Intenciones de Renunciar al Trabajo, Escala de Inseguridad Laboral y, por último, el Índice Breve de Satisfacción Laboral Afectiva, los cuales fueron validados en el contexto peruano. Finalmente, se obtuvo como resultado, mediante el Modelo Explicativo Latente, que la Satisfacción Laboral y la Insegur

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Frot, Benjamin. "Graphical model selection for Gaussian conditional random fields in the presence of latent variables : theory and application to genetics." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:0a6799ed-fca1-48b2-89cd-ad6f2c0439af.

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The task of performing graphical model selection arises in many applications in science and engineering. The field of application of interest in this thesis relates to the needs of datasets that include genetic and multivariate phenotypic data. There are several factors that make this problem particularly challenging: some of the relevant variables might not be observed, high-dimensionality might cause identifiability issues and, finally, it might be preferable to learn the model over a subset of the collection while conditioning on the rest of the variables, e.g. genetic variants. We suggest

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11

Moustaki, Irini. "Latent variable models for mixed manifest variables." Thesis, London School of Economics and Political Science (University of London), 1996. http://etheses.lse.ac.uk/78/.

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Latent variable models are widely used in social sciences in which interest is centred on entities such as attitudes, beliefs or abilities for which there e)dst no direct measuring instruments. Latent modelling tries to extract these entities, here described as latent (unobserved) variables, from measurements on related manifest (observed) variables. Methodology already exists for fitting a latent variable model to manifest data that is either categorical (latent trait and latent class analysis) or continuous (factor analysis and latent profile analysis). In this thesis a latent trait and a la

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Channarond, Antoine. "Recherche de structure dans un graphe aléatoire : modèles à espace latent." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112338/document.

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Cette thèse aborde le problème de la recherche d'une structure (ou clustering) dans lesnoeuds d'un graphe. Dans le cadre des modèles aléatoires à variables latentes, on attribue à chaque noeud i une variable aléatoire non observée (latente) Zi, et la probabilité de connexion des noeuds i et j dépend conditionnellement de Zi et Zj . Contrairement au modèle d'Erdos-Rényi, les connexions ne sont pas indépendantes identiquement distribuées; les variables latentes régissent la loi des connexions des noeuds. Ces modèles sont donc hétérogènes, et leur structure est décrite par les variables latentes

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Hatzinger, Reinhold, and Walter Katzenbeisser. "Log-linear Rasch-type models for repeated categorical data with a psychobiological application." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2008. http://epub.wu.ac.at/126/1/document.pdf.

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The purpose of this paper is to generalize regression models for repeated categorical data based on maximizing a conditional likelihood. Some existing methods, such as those proposed by Duncan (1985), Fischer (1989), and Agresti (1993, and 1997) are special cases of this latent variable approach, used to account for dependencies in clustered observations. The generalization concerns the incorporation of rather general data structures such as subject-specific time-dependent covariates, a variable number of observations per subject and time periods of arbitrary length in order to evaluate treatm

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Du, Toit Berne. "The modification, elaboration and empirical evaluation of the De Goede learning potential structural model through the incorporation of non-cognitive learning competency potential latent variables." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95793.

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Thesis (MCom)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: People are often referred to in a Human Resource Development context as the organisation‟s most important resource in recognition of the important knowledge and learning they bring to the organisation (Bierema & Eraut, 2004). South African organisations experience a shortage of this valuable and important resource due to the country‟s social political past which was led by the Apartheid system. South Africa today still suffers from the consequences of the history of racial discrimination which was lead by the Apartheid system.

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Hatefi, Armin. "Mixture model analysis with rank-based samples." Statistica Sinica, 2013. http://hdl.handle.net/1993/23849.

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Simple random sampling (SRS) is the most commonly used sampling design in data collection. In many applications (e.g., in fisheries and medical research) quantification of the variable of interest is either time-consuming or expensive but ranking a number of sampling units, without actual measurement on them, can be done relatively easy and at low cost. In these situations, one may use rank-based sampling (RBS) designs to obtain more representative samples from the underlying population and improve the efficiency of the statistical inference. In this thesis, we study the theory and application

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Aydogan, Mustafa. "The Relationship of Self-Efficacy, Self-Advocacy, and Multicultural Counseling Competency of School Counselors: A Structural Equation Model." Kent State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1625525456093709.

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Creagh-Osborne, Jane. "Latent variable generalized linear models." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/1885.

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Generalized Linear Models (GLMs) (McCullagh and Nelder, 1989) provide a unified framework for fixed effect models where response data arise from exponential family distributions. Much recent research has attempted to extend the framework to include random effects in the linear predictors. Different methodologies have been employed to solve different motivating problems, for example Generalized Linear Mixed Models (Clayton, 1994) and Multilevel Models (Goldstein, 1995). A thorough review and classification of this and related material is presented. In Item Response Theory (IRT) subjects are tes

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Dallaire, Patrick. "Bayesian nonparametric latent variable models." Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/26848.

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L’un des problèmes importants en apprentissage automatique est de déterminer la complexité du modèle à apprendre. Une trop grande complexité mène au surapprentissage, ce qui correspond à trouver des structures qui n’existent pas réellement dans les données, tandis qu’une trop faible complexité mène au sous-apprentissage, c’est-à-dire que l’expressivité du modèle est insuffisante pour capturer l’ensemble des structures présentes dans les données. Pour certains modèles probabilistes, la complexité du modèle se traduit par l’introduction d’une ou plusieurs variables cachées dont le rôle est d’exp

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Ridall, Peter Gareth. "Bayesian Latent Variable Models for Biostatistical Applications." Queensland University of Technology, 2004. http://eprints.qut.edu.au/16164/.

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In this thesis we develop several kinds of latent variable models in order to address three types of bio-statistical problem. The three problems are the treatment effect of carcinogens on tumour development, spatial interactions between plant species and motor unit number estimation (MUNE). The three types of data looked at are: highly heterogeneous longitudinal count data, quadrat counts of species on a rectangular lattice and lastly, electrophysiological data consisting of measurements of compound muscle action potential (CMAP) area and amplitude. Chapter 1 sets out the structure and th

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Ödling, David, and Arvid Österlund. "Factorisation of Latent Variables in Word Space Models : Studying redistribution of weight on latent variables." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-153776.

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The ultimate goal of any DSM is a scalable and accurate representation of lexical semantics. Recent developments due to Bullinaria & Levy (2012) and Caron (2001) indicate that the accuracy of such models can be improved by redistribution of weight on the principal components. However, this method is poorly understood and barely replicated due to the computational expensive dimension reduction and the puzzling nature of the results. This thesis aims to explore the nature of these results. Beginning by reproducing the results in Bullinaria & Levy (2012) we move onto deepen the understand

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Lee, Christina (Christina Esther). "Latent variable model estimation via collaborative filtering." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113932.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 255-264).<br>Similarity based collaborative filtering for matrix completion is a popular heuristic that has been used widely across industry in the previous decades to build recommendation systems, due to its simplicity and s

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Jung, Sunho. "Regularized structural equation models with latent variables." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66858.

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In structural equation models with latent variables, maximum likelihood (ML) estimation is currently the most prevailing estimation method. However, the ML method fails to provide accurate solutions in a number of situations including those involving small sample sizes, nonnormality, and model misspecification. To over come these difficulties, regularized extensions of two-stage least squares estimation are proposed that incorporate a ridge type of regularization in the estimation of parameters. Two simulation studies and two empirical applications demonstrate that the propos

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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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O'Sullivan, Aidan Michael. "Bayesian latent variable models with applications." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/19191.

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The massive increases in computational power that have occurred over the last two decades have contributed to the increasing prevalence of Bayesian reasoning in statistics. The often intractable integrals required as part of the Bayesian approach to inference can be approximated or estimated using intensive sampling or optimisation routines. This has extended the realm of applications beyond simple models for which fully analytic solutions are possible. Latent variable models are ideally suited to this approach as it provides a principled method for resolving one of the more difficult issues a

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Zhang, Cheng. "Structured Representation Using Latent Variable Models." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191455.

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Over the past two centuries the industrial revolution automated a great part of work that involved human muscles. Recently, since the beginning of the 21st century, the focus has shifted towards automating work that is involving our brain to further improve our lives. This is accomplished by establishing human-level intelligence through machines, which lead to the growth of the field of artificial intelligence. Machine learning is a core component of artificial intelligence. While artificial intelligence focuses on constructing an entire intelligence system, machine learning focuses on the lea

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Paquet, Ulrich. "Bayesian inference for latent variable models." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613111.

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Parsons, S. "Approximation methods for latent variable models." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1513250/.

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Modern statistical models are often intractable, and approximation methods can be required to perform inference on them. Many different methods can be employed in most contexts, but not all are fully understood. The current thesis is an investigation into the use of various approximation methods for performing inference on latent variable models. Composite likelihoods are used as surrogates for the likelihood function of state space models (SSM). In chapter 3, variational approximations to their evaluation are investigated, and the interaction of biases as composite structure changes is observ

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KAVTARADZE, LASHA. "DINAMICS AND LATENT VARIABLES IN APPLIED MACROECONOMICS." Doctoral thesis, Università Cattolica del Sacro Cuore, 2016. http://hdl.handle.net/10280/16793.

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La tesi di dottorato, composta da tre capitoli, si concentra sulla valutazione delle dinamiche di inflazione in Georgia e sulla previsione dei tassi di cambio nominali per i Paesi della European Eastern Partnership attraverso l’utilizzo di moderne tecniche econometriche. Nel primo capitolo, abbiamo svolto un’indagine sui modelli di previsione dei tassi di cambio e dell’inflazione. Questa indagine rivela che i modelli “factor-based and time-varying parameter” generano migliori previsioni rispetto ad altri modelli. Nel secondo capitolo, abbiamo approfondito le dinamiche di inflazione in Georgia

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Wegelin, Jacob A. "Latent models for cross-covariance /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/8982.

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Strohe, Hans Gerhard. "Dynamic latent variables path models : an alternative PLS estimation." Universität Potsdam, 1995. http://opus.kobv.de/ubp/volltexte/2009/2949/.

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In this paper a partial least squares (PLS) approach to dynamic modelling with latent variables is proposed. Let Y be a matrix of manifest variables and H the matrix of the corresponding latent variables. And let H = BH+ε be a structural PLS model with a coefficient matrix B. Then this model can be made a dynamic one by substituting for B a matrix F = B + CL containing the lag operator L. Then the structural dynamic model H = FH+ε is formally estimated like an ordinary PLS model. In an exploratory way the model can be used for forecasting purposes. The procedure is being programmed in ISP.

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Kao, Ling-Jing. "Data augmentation for latent variables in marketing." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155653751.

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Dominicus, Annica. "Latent variable models for longitudinal twin data." Doctoral thesis, Stockholm : Mathematical statistics, Dept. of mathematics, Stockholm university, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-848.

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Burridge, C. Y. "Latent variable models for genotype-environment interaction." Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383469.

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Fusi, Nicolo. "Probabilistic latent variable models in statistical genomics." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8326/.

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In this thesis, we propose different probabilistic latent variable mod- els to identify and capture the hidden structure present in commonly studied genomics datasets. We start by investigating how to cor- rect for unwanted correlations due to hidden confounding factors in gene expression data. This is particularly important in expression quantitative trait loci (eQTL) studies, where the goal is to identify associations between genetic variants and gene expression levels. We start with a na¨ ıve approach, which estimates the latent factors from the gene expression data alone, ignoring the gene

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Albanese, Maria Teresinha. "Latent variable models for binary response data." Thesis, London School of Economics and Political Science (University of London), 1990. http://etheses.lse.ac.uk/1220/.

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Most of the results in this thesis are obtained for the logit/probit model for binary response data given by Bartholomew (1980), which is sometimes called the two-parameter logistic model. In most the cases the results also hold for other common binary response models. By profiling and an approximation, we investigate the behaviour of the likelihood function, to see if it is suitable for ML estimation. Particular attention is given to the shape of the likelihood around the maximum point in order to see whether the information matrix will give a good guide to the variability of the estimates. T

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Podosinnikova, Anastasia. "Sur la méthode des moments pour l'estimation des modèles à variables latentes." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEE050/document.

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Les modèles linéaires latents sont des modèles statistique puissants pour extraire la structure latente utile à partir de données non structurées par ailleurs. Ces modèles sont utiles dans de nombreuses applications telles que le traitement automatique du langage naturel et la vision artificielle. Pourtant, l'estimation et l'inférence sont souvent impossibles en temps polynomial pour de nombreux modèles linéaires latents et on doit utiliser des méthodes approximatives pour lesquelles il est difficile de récupérer les paramètres. Plusieurs approches, introduites récemment, utilisent la méthode

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Chen, Tao. "Search-based learning of latent tree models /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20CHEN.

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Wang, Yi. "Latent tree models for multivariate density estimation : algorithms and applications /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20WANGY.

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Ruffini, Matteo. "Learning latent variable models : efficient algorithms and applications." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/665817.

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Learning latent variable models is a fundamental machine learning problem, and the models belonging to this class - which include topic models, hidden Markov models, mixture models and many others - have a variety of real-world applications, like text mining, clustering and time series analysis. For many practitioners, the decade-old Expectation Maximization method (EM) is still the tool of choice, despite its known proneness to local minima and long running times. To overcome these issues, algorithms based on the spectral method of moments have been recently proposed. These techniques recove

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Harvey, Morgan A. "Bayesian latent variable models for the collaborative Web." Thesis, University of Strathclyde, 2011. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=16822.

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Rastelli, Riccardo, and Nial Friel. "Optimal Bayesian estimators for latent variable cluster models." Springer Nature, 2018. http://dx.doi.org/10.1007/s11222-017-9786-y.

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In cluster analysis interest lies in probabilistically capturing partitions of individuals, items or observations into groups, such that those belonging to the same group share similar attributes or relational profiles. Bayesian posterior samples for the latent allocation variables can be effectively obtained in a wide range of clustering models, including finite mixtures, infinite mixtures, hidden Markov models and block models for networks. However, due to the categorical nature of the clustering variables and the lack of scalable algorithms, summary tools that can interpret such samples ar

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Qiu, David. "Embedding and latent variable models using maximal correlation." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108977.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 45-46).<br>Finding low dimensional latent variable models is a useful technique in inferring unobserved affinity between unobserved co-occurrences. We explore using maximal correlation and the alternating conditional expectation algorithm to construct embeddings one dimensional at a time to maximally preserve the linear correlation in the embedding space. Each dimension is enforced to be orth

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Svebrant, Henrik. "Latent variable neural click models for web search." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232311.

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User click modeling in web search is most commonly done through probabilistic graphical models. Due to the successful use of machine learning techniques in other fields of research, it is interesting to evaluate how machine learning can be applied to click modeling. In this thesis, modeling is done using recurrent neural networks trained on a distributed representation of the state of the art user browsing model (UBM). It is further evaluated how extending this representation with a set of latent variables that are easily derivable from click logs, can affect the model's prediction performance

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Dupuy, Christophe. "Inference and applications for topic models." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE055/document.

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La plupart des systèmes de recommandation actuels se base sur des évaluations sous forme de notes (i.e., chiffre entre 0 et 5) pour conseiller un contenu (film, restaurant...) à un utilisateur. Ce dernier a souvent la possibilité de commenter ce contenu sous forme de texte en plus de l'évaluer. Il est difficile d'extraire de l'information d'un texte brut tandis qu'une simple note contient peu d'information sur le contenu et l'utilisateur. Dans cette thèse, nous tentons de suggérer à l'utilisateur un texte lisible personnalisé pour l'aider à se faire rapidement une opinion à propos d'un contenu

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Heiss, Florian. "Essays on Specification and Estimation of Latent Variable Models." [S.l. : s.n.], 2005. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11947811.

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Hore, Victoria. "Latent variable models for analysing multidimensional gene expression data." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:ec62bc11-5c3f-467d-9ff3-f3c4eb29d140.

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Multi-tissue gene expression studies give rise to 3D arrays of data. These experiments make it possible to study the tissue-specific nature of gene regulation and also the relationship between genotypes and higher level traits such as disease status. Analysing these multidimensional data sets is a statistical challenge, as they contain high noise levels and missing data. In this thesis I introduce a new approach for analysing multidimensional gene expression data sets called SPIDER (SParse Integrated DEcomposition for RNA-sequencing). SPIDER is a sparse Bayesian tensor decomposition that model

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Newman, Keith. "Bayesian modelling of latent Gaussian models featuring variable selection." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3700.

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Latent Gaussian models are popular and versatile models for performing Bayesian inference. In many cases, these models will be analytically intractable creating a need for alternative inference methods. Integrated nested Laplace approximations (INLA) provides fast, deterministic inference of approximate posterior densities by exploiting sparsity in the latent structure of the model. Markov chain Monte Carlo (MCMC) is often used for Bayesian inference by sampling from a target posterior distribution. This suffers poor mixing when many variables are correlated, but careful reparameterisation or

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Lu, Yu. "Statistical and Computational Guarantees for Learning Latent Variable Models." Thesis, Yale University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10783452.

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<p> Latent variable models are widely used to capture the underlying structures of the data, for example, Gaussian mixture models for speech recognition, stochastic block models for community detection and topic models for information retrieval. While alternative minimization based algorithms such as EM algorithm and Lloyd's algorithm performs well in practice, there has been little theoretical advancement in explaining the effectiveness of these algorithms. In this thesis, we investigate the performance of Lloyd's algorithm and EM algorithm on clustering two-mixture of Gaussians. With an init

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Ramalho, Fernandes Salgueiro Maria de FaÌtima. "Distributions of test statistics for edge exclusion for graphical models." Thesis, University of Southampton, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252225.

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Bouscasse, Hélène. "Essays on travel mode choice modeling : a discrete choice approach of the interactions between economic and behavioral theories." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE2106/document.

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Cette thèse a pour objectif d’incorporer des éléments de théories de psychologie et d’économie comportementale dans des modèles de choix discret afin d’améliorer la compréhension du choix modal réalisé à l’échelle régionale. Les estimations se basent sur une enquête de type choice experiment présentée en première partie. Une deuxième partie s’intéresse à l’incorporation de variables latentes pour expliquer le choix modal. Après une revue de littérature sur les modèles de choix hybrides, c’est-à-dire des modèles combinant modèle d’équations structurelles et modèle de choix discret, un tel modèl

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