Relevant bibliographies by topics / Processus gaussiens latents

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Processus gaussiens latents'

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Published: 10 November 2022

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Journal articles on the topic "Processus gaussiens latents"

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Chaudhary, Neha, and Priti Dimri. "LATENT FINGERPRINT IMAGE ENHANCEMENT BASED ON OPTIMIZED BENT IDENTITY BASED CONVOLUTIONAL NEURAL NETWORK." Indian Journal of Computer Science and Engineering 12, no. 5 (October 20, 2021): 1477–93. http://dx.doi.org/10.21817/indjcse/2021/v12i5/211205124.

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Fingerprints are unique biometric systems (BSs) in which none of the human possesses similar fingerprint structures. It is one of the most significant biometric processes used in the identification of criminals. Latent fingerprints or latents are generated mainly by the finger sweat or oil deposits which is left by the suspects unintentionally. The impressions of latents are blurred or smudgy in nature and not viewed by naked eye. These fingerprints are of low quality, corrupted by noise, degraded by technological factors and exhibit minor details. Latents display consistent structural info when observed as an image. Image Enhancement is necessary in latents, to transform the latent (noisy) image into fine-quality (enhanced) image. In this work, a new image enhancement approach named BI-CNN (Bent Identity-Convolution Neural Network) with Spatial Pyramid Max Pooling (SPMP) model optimized using TSOA (Tunicate Swarm Optimization Algorithm) is presented to produce an enhanced latent at the output. This procedure involves the integration of ROI (Region Of Interest) Estimation, Anisotropic Gaussian Filter (AGF) based Pre-filtering, Fingerprint alignment using Sobel Filter, Intrinsic Feature patch extraction using Optimized BI-CNN, GAT (Graph Attention) network based Similarity Estimation followed by image reconstruction and feedback module. The implementation tool used in this work is PYTHON platform. The proposed optimized BI-CNN framework tested on dual public datasets namely IIITD-latent finger print and IIITD-MOLF have shown enhanced outcomes. Thus, the IIITD -latent fingerprint database obtained 83.33% on Rank-10 accuracy and 39.33% on Rank-25 accuracy.
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Alvarez, M. A., D. Luengo, and N. D. Lawrence. "Linear Latent Force Models Using Gaussian Processes." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 11 (November 2013): 2693–705. http://dx.doi.org/10.1109/tpami.2013.86.

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Oune, Nicholas, and Ramin Bostanabad. "Latent map Gaussian processes for mixed variable metamodeling." Computer Methods in Applied Mechanics and Engineering 387 (December 2021): 114128. http://dx.doi.org/10.1016/j.cma.2021.114128.

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Panos, Aristeidis, Petros Dellaportas, and Michalis K. Titsias. "Large scale multi-label learning using Gaussian processes." Machine Learning 110, no. 5 (April 14, 2021): 965–87. http://dx.doi.org/10.1007/s10994-021-05952-5.

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AbstractWe introduce a Gaussian process latent factor model for multi-label classification that can capture correlations among class labels by using a small set of latent Gaussian process functions. To address computational challenges, when the number of training instances is very large, we introduce several techniques based on variational sparse Gaussian process approximations and stochastic optimization. Specifically, we apply doubly stochastic variational inference that sub-samples data instances and classes which allows us to cope with Big Data. Furthermore, we show it is possible and beneficial to optimize over inducing points, using gradient-based methods, even in very high dimensional input spaces involving up to hundreds of thousands of dimensions. We demonstrate the usefulness of our approach on several real-world large-scale multi-label learning problems.
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Hall, Peter, Hans-Georg Mller, and Fang Yao. "Modelling sparse generalized longitudinal observations with latent Gaussian processes." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70, no. 4 (September 2008): 703–23. http://dx.doi.org/10.1111/j.1467-9868.2008.00656.x.

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Mattos, César Lincoln C., Andreas Damianou, Guilherme A. Barreto, and Neil D. Lawrence. "Latent Autoregressive Gaussian Processes Models for Robust System Identification." IFAC-PapersOnLine 49, no. 7 (2016): 1121–26. http://dx.doi.org/10.1016/j.ifacol.2016.07.353.

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Gammelli, Daniele, Inon Peled, Filipe Rodrigues, Dario Pacino, Haci A. Kurtaran, and Francisco C. Pereira. "Estimating latent demand of shared mobility through censored Gaussian Processes." Transportation Research Part C: Emerging Technologies 120 (November 2020): 102775. http://dx.doi.org/10.1016/j.trc.2020.102775.

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Dew, Ryan, Asim Ansari, and Yang Li. "Modeling Dynamic Heterogeneity Using Gaussian Processes." Journal of Marketing Research 57, no. 1 (October 14, 2019): 55–77. http://dx.doi.org/10.1177/0022243719874047.

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Marketing research relies on individual-level estimates to understand the rich heterogeneity of consumers, firms, and products. While much of the literature focuses on capturing static cross-sectional heterogeneity, little research has been done on modeling dynamic heterogeneity, or the heterogeneous evolution of individual-level model parameters. In this work, the authors propose a novel framework for capturing the dynamics of heterogeneity, using individual-level, latent, Bayesian nonparametric Gaussian processes. Similar to standard heterogeneity specifications, this Gaussian process dynamic heterogeneity (GPDH) specification models individual-level parameters as flexible variations around population-level trends, allowing for sharing of statistical information both across individuals and within individuals over time. This hierarchical structure provides precise individual-level insights regarding parameter dynamics. The authors show that GPDH nests existing heterogeneity specifications and that not flexibly capturing individual-level dynamics may result in biased parameter estimates. Substantively, they apply GPDH to understand preference dynamics and to model the evolution of online reviews. Across both applications, they find robust evidence of dynamic heterogeneity and illustrate GPDH’s rich managerial insights, with implications for targeting, pricing, and market structure analysis.
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Zhang, Dongmei, Yuyang Zhang, Bohou Jiang, Xinwei Jiang, and Zhijiang Kang. "Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching." Energies 13, no. 17 (August 19, 2020): 4290. http://dx.doi.org/10.3390/en13174290.

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Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.
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Lu, Chi-Ken, and Patrick Shafto. "Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning." Entropy 23, no. 11 (November 20, 2021): 1545. http://dx.doi.org/10.3390/e23111545.

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Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, it was pointed out that the hierarchical structure of DGP well suited modeling the multi-fidelity regression, in which one is provided sparse observations with high precision and plenty of low fidelity observations. We propose the conditional DGP model in which the latent GPs are directly supported by the fixed lower fidelity data. Then the moment matching method is applied to approximate the marginal prior of conditional DGP with a GP. The obtained effective kernels are implicit functions of the lower-fidelity data, manifesting the expressivity contributed by distribution propagation within the hierarchy. The hyperparameters are learned via optimizing the approximate marginal likelihood. Experiments with synthetic and high dimensional data show comparable performance against other multi-fidelity regression methods, variational inference, and multi-output GP. We conclude that, with the low fidelity data and the hierarchical DGP structure, the effective kernel encodes the inductive bias for true function allowing the compositional freedom.
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Dissertations / Theses on the topic "Processus gaussiens latents"

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Cuesta, Ramirez Jhouben Janyk. "Optimization of a computationally expensive simulator with quantitative and qualitative inputs." Thesis, Lyon, 2022. http://www.theses.fr/2022LYSEM010.

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Dans cette thèse, les problèmes mixtes couteux sont abordés par le biais de processus gaussiens où les variables discrètes sont relaxées en variables latentes continues. L'espace continu est plus facilement exploité par les techniques classiques d'optimisation bayésienne que ne le serait un espace mixte. Les variables discrètes sont récupérées soit après l'optimisation continue, soit simultanément avec une contrainte supplémentaire de compatibilité continue-discrète qui est traitée avec des lagrangiens augmentés. Plusieurs implémentations possibles de ces optimiseurs mixtes bayésiens sont comparées. En particulier, la reformulation du problème avec des variables latentes continues est mise en concurrence avec des recherches travaillant directement dans l'espace mixte. Parmi les algorithmes impliquant des variables latentes et un lagrangien augmenté, une attention particulière est consacrée aux multiplicateurs de lagrange pour lesquels des techniques d'estimation locale et globale sont étudiées. Les comparaisons sont basées sur l'optimisation répétée de trois fonctions analytiques et sur une application mécanique concernant la conception d'une poutre. Une étude supplémentaire pour l'application d'une stratégie d'optimisation mixte proposée dans le domaine de l'auto-calibrage mixte est faite. Cette analyse s'inspire d'une application de quantification des radionucléides, qui définit une fonction inverse spécifique nécessitant l'étude de ses multiples propriétés dans le scenario continu. une proposition de différentes stratégies déterministes et bayésiennes a été faite en vue d'une définition complète dans un contexte de variables mixtes
In this thesis, costly mixed problems are approached through gaussian processes where the discrete variables are relaxed into continuous latent variables. the continuous space is more easily harvested by classical bayesian optimization techniques than a mixed space would. discrete variables are recovered either subsequently to the continuous optimization, or simultaneously with an additional continuous-discrete compatibility constraint that is handled with augmented lagrangians. several possible implementations of such bayesian mixed optimizers are compared. in particular, the reformulation of the problem with continuous latent variables is put in competition with searches working directly in the mixed space. among the algorithms involving latent variables and an augmented lagrangian, a particular attention is devoted to the lagrange multipliers for which a local and a global estimation techniques are studied. the comparisons are based on the repeated optimization of three analytical functions and a mechanical application regarding a beam design. an additional study for applying a proposed mixed optimization strategy in the field of mixed self-calibration is made. this analysis was inspired in an application in radionuclide quantification, which defined an specific inverse function that required the study of its multiple properties in the continuous scenario. a proposition of different deterministic and bayesian strategies was made towards a complete definition in a mixed variable setup
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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 implementieren. Inferenz in probabilistischen Modellen bedeutet die A-Posteriori-Verteilung der latenten Variablen, gegeben der Daten, zu berechnen. Die meisten interessanten latenten Gauß-Prozess-Modelle haben zurzeit nur begrenzte Anwendungsmöglichkeiten auf großen Datensätzen. In dieser Doktorarbeit stellen wir eine neue effiziente Inferenzmethode für latente Gauß-Prozess-Modelle vor. Unser neuer Ansatz, den wir augmented variational inference nennen, basiert auf der Idee, eine erweiterte (augmented) Version des Gauß-Prozess-Modells zu betrachten, welche bedingt konjugiert (conditionally conjugate) ist. Wir zeigen, dass Inferenz in dem erweiterten Modell effektiver ist und dass alle Schritte des variational inference Algorithmus in geschlossener Form berechnet werden können, was mit früheren Ansätzen nicht möglich war. Unser neues Inferenzkonzept ermöglicht es, neue latente Gauß-Prozess- Modelle zu studieren, die zu innovativen Ergebnissen im Bereich der Sprachmodellierung, genetischen Assoziationsstudien und Quantifizierung der Unsicherheit in Klassifikationsproblemen führen.
Latent Gaussian process (GP) models help scientists to uncover hidden structure in data, express domain knowledge and form predictions about the future. These models have been successfully applied in many domains including robotics, geology, genetics and medicine. A GP defines a distribution over functions and can be used as a flexible building block to develop expressive probabilistic models. The main computational challenge of these models is to make inference about the unobserved latent random variables, that is, computing the posterior distribution given the data. Currently, most interesting Gaussian process models have limited applicability to big data. This thesis develops a new efficient inference approach for latent GP models. Our new inference framework, which we call augmented variational inference, is based on the idea of considering an augmented version of the intractable GP model that renders the model conditionally conjugate. We show that inference in the augmented model is more efficient and, unlike in previous approaches, all updates can be computed in closed form. The ideas around our inference framework facilitate novel latent GP models that lead to new results in language modeling, genetic association studies and uncertainty quantification in classification tasks.
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Hartmann, Marcelo. "Métodos de Monte Carlo Hamiltoniano na inferência Bayesiana não-paramétrica de valores extremos." Universidade Federal de São Carlos, 2015. https://repositorio.ufscar.br/handle/ufscar/4601.

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In this work we propose a Bayesian nonparametric approach for modeling extreme value data. We treat the location parameter _ of the generalized extreme value distribution as a random function following a Gaussian process model (Rasmussem & Williams 2006). This configuration leads to no closed-form expressions for the highdimensional posterior distribution. To tackle this problem we use the Riemannian Manifold Hamiltonian Monte Carlo algorithm which allows samples from the posterior distribution with complex form and non-usual correlation structure (Calderhead & Girolami 2011). Moreover, we propose an autoregressive time series model assuming the generalized extreme value distribution for the noise and obtained its Fisher information matrix. Throughout this work we employ some computational simulation studies to assess the performance of the algorithm in its variants and show many examples with simulated and real data-sets.
Neste trabalho propomos uma abordagem Bayesiana não-paramétrica para a modelagem de dados com comportamento extremo. Tratamos o parâmetro de locação _ da distribuição generalizada de valor extremo como uma função aleatória e assumimos um processo Gaussiano para tal função (Rasmussem & Williams 2006). Esta situação leva à intratabilidade analítica da distribuição a posteriori de alta dimensão. Para lidar com este problema fazemos uso do método Hamiltoniano de Monte Carlo em variedade Riemanniana que permite a simulação de valores da distribuição a posteriori com forma complexa e estrutura de correlação incomum (Calderhead & Girolami 2011). Além disso, propomos um modelo de série temporal autoregressivo de ordem p, assumindo a distribuição generalizada de valor extremo para o ruído e determinamos a respectiva matriz de informação de Fisher. No decorrer de todo o trabalho, estudamos a qualidade do algoritmo em suas variantes através de simulações computacionais e apresentamos vários exemplos com dados reais e simulados.
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Karipidou, Kelly. "Modelling the body language of a musical conductor using Gaussian Process Latent Variable Models." Thesis, KTH, Datorseende och robotik, CVAP, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-176101.

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Motion capture data of a musical conductor's movements when conducting a string quartet is analysed in this work using the Gaussian Process Latent Variable Model (GP-LVM) framework. A dimensionality reduction on the high dimensional motion capture data to a two dimensional representation using a GP-LVM is performed, followed by classification of conduction movements belonging to different interpretations of the same musical piece. A dynamical prior is used for the GP-LVM, resulting in a representative latent space for the sequential conduction motion data. Classification results with great performance for some of the interpretations are obtained. The GP-LVM with dynamical prior distribution is shown to be a reasonable choice when wanting to model conduction data, opening up the possibility for creating for example a "conduct-your-own-orchestra" system in a principled mathematical way, in the future.
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Mendes, Armando Praça. "A gestão da estratégia mercadologica sob uma nova perspectiva: existe relação entre a física e a administração?" reponame:Repositório Institucional do FGV, 2004. http://hdl.handle.net/10438/3884.

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A Física e a Administração concentram suas pesquisas sobre fenômenos que, de certa forma, se assemelham, fazendo com que nos questionemos a respeito da grande integral do universo a que estamos submetidos. Em uma exploração por analogias, aproxima-se aqui o mundo organizacional ao dos sistemas UnIVerSaIS, instáveis e não-integráveis, onde a flecha do tempo é quem determina a evolução dos mesmos. Mostra-se que na Administração, como na Física, tudo parece convergir na direção de um inesgotável repertório de bifurcações e possibilidades para o destino mercadológico de produtos, serviços e marcas ao longo de um continuum. Para amenizar os efeitos dessas incertezas, é buscada uma simplificação desses complexos sistemas sociais através de uma proposta de modelo baseado em fatores consagrados pela literatura da gestão empresarial como norteadores das escolhas dos consumidores; um processo gaussiano da 'percepção do valor', que pode servir de ferramenta nas decisões estratégicas e gerenciais dentro das empresas.
The physical and the administration sciences focus their researches on phenomenum wich, in some ways, can have similarities, making us to question and ask about the great convergence ofthe systems in the Universe under which we are submitted. Exploring by analogues, this research tries to make sense to put together the organizational and physical systems, unstables and not integratable, moving forward by the time's arrow, that determines the evolution ofthose. In the Administration, as in the Physics, everything seems to converge at the direction of an inexhaustible collection of forks and possibilities, if considering the destiny of products, services and labels during the human history. To soften the effects of those uncertanties, it is fetched a simplification of these complex social systems across a proposal of a model to be constructed and tested, based in some factors established by business management's literature as the guiders of the consumers's choices; a gaussian process of the 'insight value', that can be useful as a tool for the strategic and business managing decisions beyond the companies.
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Sauer, Patrick Martin. "Model-based understanding of facial expressions." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/modelbased-understanding-of-facial-expressions(e88bff4f-d72e-4d11-b964-fc20f009609b).html.

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In this thesis we present novel methods for constructing and fitting 2d models of shape and appearance which are used for analysing human faces. The first contribution builds on previous work on discriminative fitting strategies for active appearance models (AAMs) in which regression models are trained to predict the location of shapes based on texture samples. In particular, we investigate non-parametric regression methods including random forests and Gaussian processes which are used together with gradient-like features for shape model fitting. We then develop two training algorithms which combine such models into sequences, and systematically compare their performance to existing linear generative AAM algorithms. Inspired by the performance of the Gaussian process-based regression methods, we investigate a group of non-linear latent variable models known as Gaussian process latent variable models (GPLVM). We discuss how such models may be used to develop a generative active appearance model algorithm whose texture model component is non-linear, and show how this leads to lower-dimensional models which are capable of generating more natural-looking images of faces when compared to equivalent linear models. We conclude by describing a novel supervised non-linear latent variable model based on Gaussian processes which we apply to the problem of recognising emotions from facial expressions.
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Hall, Otto. "Inference of buffer queue times in data processing systems using Gaussian Processes : An introduction to latency prediction for dynamic software optimization in high-end trading systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214791.

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This study investigates whether Gaussian Process Regression can be applied to evaluate buffer queue times in large scale data processing systems. It is additionally considered whether high-frequency data stream rates can be generalized into a small subset of the sample space. With the aim of providing basis for dynamic software optimization, a promising foundation for continued research is introduced. The study is intended to contribute to Direct Market Access financial trading systems which processes immense amounts of market data daily. Due to certain limitations, we shoulder a naïve approach and model latencies as a function of only data throughput in eight small historical intervals. The training and test sets are represented from raw market data, and we resort to pruning operations to shrink the datasets by a factor of approximately 0.0005 in order to achieve computational feasibility. We further consider four different implementations of Gaussian Process Regression. The resulting algorithms perform well on pruned datasets, with an average R2 statistic of 0.8399 over six test sets of approximately equal size as the training set. Testing on non-pruned datasets indicate shortcomings from the generalization procedure, where input vectors corresponding to low-latency target values are associated with less accuracy. We conclude that depending on application, the shortcomings may be make the model intractable. However for the purposes of this study it is found that buffer queue times can indeed be modelled by regression algorithms. We discuss several methods for improvements, both in regards to pruning procedures and Gaussian Processes, and open up for promising continued research.
Denna studie undersöker huruvida Gaussian Process Regression kan appliceras för att utvärdera buffer-kötider i storskaliga dataprocesseringssystem. Dessutom utforskas ifall dataströmsfrekvenser kan generaliseras till en liten delmängd av utfallsrymden. Medmålet att erhålla en grund för dynamisk mjukvaruoptimering introduceras en lovandestartpunkt för fortsatt forskning. Studien riktas mot Direct Market Access system för handel på finansiella marknader, somprocesserar enorma mängder marknadsdata dagligen. På grund av vissa begränsningar axlas ett naivt tillvägagångssätt och väntetider modelleras som en funktion av enbartdatagenomströmning i åtta små historiska tidsinterval. Tränings- och testdataset representeras från ren marknadsdata och pruning-tekniker används för att krympa dataseten med en ungefärlig faktor om 0.0005, för att uppnå beräkningsmässig genomförbarhet. Vidare tas fyra olika implementationer av Gaussian Process Regression i beaktning. De resulterande algorithmerna presterar bra på krympta dataset, med en medel R2 statisticpå 0.8399 över sex testdataset, alla av ungefär samma storlek som träningsdatasetet. Tester på icke krympta dataset indikerar vissa brister från pruning, där input vektorermotsvararande låga latenstider är associerade med mindre exakthet. Slutsatsen dras att beroende på applikation kan dessa brister göra modellen obrukbar. För studiens syftefinnes emellertid att latenstider kan sannerligen modelleras av regressionsalgoritmer. Slutligen diskuteras metoder för förbättrning med hänsyn till både pruning och GaussianProcess Regression, och det öppnas upp för lovande vidare forskning.
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Qian, Zhiguang. "Computer experiments [electronic resource] : design, modeling and integration /." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11480.

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The use of computer modeling is fast increasing in almost every scientific, engineering and business arena. This dissertation investigates some challenging issues in design, modeling and analysis of computer experiments, which will consist of four major parts. In the first part, a new approach is developed to combine data from approximate and detailed simulations to build a surrogate model based on some stochastic models. In the second part, we propose some Bayesian hierarchical Gaussian process models to integrate data from different types of experiments. The third part concerns the development of latent variable models for computer experiments with multivariate response with application to data center temperature modeling. The last chapter is devoted to the development of nested space-filling designs for multiple experiments with different levels of accuracy.
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Ancelet, Sophie. "Exploiter l'approche hiérarchique bayésienne pour la modélisation statistique de structures spatiales: application en écologie des populations." Phd thesis, AgroParisTech, 2008. http://pastel.archives-ouvertes.fr/pastel-00004396.

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Dans la plupart des questions écologiques, les phénomènes aléatoires d'intérêt sont spatialement structurés et issus de l'effet combiné de multiples variables aléatoires, observées ou non, et inter-agissant à diverses échelles. En pratique, dès lors que les données de terrain ne peuvent être directement traitées avec des structures spatiales standards, les observations sont généralement considérées indépendantes. Par ailleurs, les modèles utilisés sont souvent basés sur des hypothèses simplificatrices trop fortes par rapport à la complexité des phénomènes étudiés. Dans ce travail, la démarche de modélisation hiérarchique est combinée à certains outils de la statistique spatiale afin de construire des structures aléatoires fonctionnelles "sur-mesure" permettant de représenter des phénomènes spatiaux complexes en écologie des populations. L'inférence de ces différents modèles est menée dans le cadre bayésien avec des algorithmes MCMC. Dans un premier temps, un modèle hiérarchique spatial (Geneclust) est développé pour identifier des populations génétiquement homogènes quand la diversité génétique varie continûment dans l'espace. Un champ de Markov caché, qui modélise la structure spatiale de la diversité génétique, est couplé à un modèle bivarié d'occurrence de génotypes permettant de tenir compte de l'existence d'unions consanguines chez certaines populations naturelles. Dans un deuxième temps, un processus de Poisson composé particulier,appelé loi des fuites, est présenté sous l'angle de vue hiérarchique pour décrire le processus d'échantillonnage d'organismes vivants. Il permet de traiter le délicat problème de données continues présentant une forte proportion de zéros et issues d'échantillonnages à efforts variables. Ce modèle est également couplé à différents modèles sur grille (spatiaux, régionalisés) afin d'introduire des dépendances spatiales entre unités géographiques voisines puis, à un champ géostatistique bivarié construit par convolution sur grille discrète afin de modéliser la répartition spatiale conjointe de deux espèces. Les capacités d'ajustement et de prédiction des différents modèles hiérarchiques proposés sont comparées aux modèles traditionnellement utilisés à partir de simulations et de jeux de données réelles (ours bruns de Suède, invertébrés épibenthiques du Golfe-du-Saint-Laurent (Canada)).
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Wang, Xiaojing. "Bayesian Modeling Using Latent Structures." Diss., 2012. http://hdl.handle.net/10161/5848.

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This dissertation is devoted to modeling complex data from the

Bayesian perspective via constructing priors with latent structures.

There are three major contexts in which this is done -- strategies for

the analysis of dynamic longitudinal data, estimating

shape-constrained functions, and identifying subgroups. The

methodology is illustrated in three different

interdisciplinary contexts: (1) adaptive measurement testing in

education; (2) emulation of computer models for vehicle crashworthiness; and (3) subgroup analyses based on biomarkers.

Chapter 1 presents an overview of the utilized latent structured

priors and an overview of the remainder of the thesis. Chapter 2 is

motivated by the problem of analyzing dichotomous longitudinal data

observed at variable and irregular time points for adaptive

measurement testing in education. One of its main contributions lies

in developing a new class of Dynamic Item Response (DIR) models via

specifying a novel dynamic structure on the prior of the latent

trait. The Bayesian inference for DIR models is undertaken, which

permits borrowing strength from different individuals, allows the

retrospective analysis of an individual's changing ability, and

allows for online prediction of one's ability changes. Proof of

posterior propriety is presented, ensuring that the objective

Bayesian analysis is rigorous.

Chapter 3 deals with nonparametric function estimation under

shape constraints, such as monotonicity, convexity or concavity. A

motivating illustration is to generate an emulator to approximate a computer

model for vehicle crashworthiness. Although Gaussian processes are

very flexible and widely used in function estimation, they are not

naturally amenable to incorporation of such constraints. Gaussian

processes with the squared exponential correlation function have the

interesting property that their derivative processes are also

Gaussian processes and are jointly Gaussian processes with the

original Gaussian process. This allows one to impose shape constraints

through the derivative process. Two alternative ways of incorporating derivative

information into Gaussian processes priors are proposed, with one

focusing on scenarios (important in emulation of computer

models) in which the function may have flat regions.

Chapter 4 introduces a Bayesian method to control for multiplicity

in subgroup analyses through tree-based models that limit the

subgroups under consideration to those that are a priori plausible.

Once the prior modeling of the tree is accomplished, each tree will

yield a statistical model; Bayesian model selection analyses then

complete the statistical computation for any quantity of interest,

resulting in multiplicity-controlled inferences. This research is

motivated by a problem of biomarker and subgroup identification to

develop tailored therapeutics. Chapter 5 presents conclusions and

some directions for future research.


Dissertation
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Book chapters on the topic "Processus gaussiens latents"

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Fantinato, Denis G., Leonardo T. Duarte, Bertrand Rivet, Bahram Ehsandoust, Romis Attux, and Christian Jutten. "Gaussian Processes for Source Separation in Overdetermined Bilinear Mixtures." In Latent Variable Analysis and Signal Separation, 300–309. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53547-0_29.

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Nickisch, Hannes, and Carl Edward Rasmussen. "Gaussian Mixture Modeling with Gaussian Process Latent Variable Models." In Lecture Notes in Computer Science, 272–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15986-2_28.

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Xiao, Zedong, Junli Zhao, Xuejun Qiao, and Fuqing Duan. "Craniofacial Reconstruction Using Gaussian Process Latent Variable Models." In Computer Analysis of Images and Patterns, 456–64. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23192-1_38.

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Nirwan, Rajbir S., and Nils Bertschinger. "Applications of Gaussian Process Latent Variable Models in Finance." In Advances in Intelligent Systems and Computing, 1209–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29513-4_87.

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Lv, Fengmao, Guowu Yang, Jinzhao Wu, Chuan Liu, and Yuhong Yang. "Anomaly Detection for Categorical Observations Using Latent Gaussian Process." In Neural Information Processing, 285–96. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70139-4_29.

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Bütepage, Judith, Lucas Maystre, and Mounia Lalmas. "Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty." In Machine Learning and Knowledge Discovery in Databases. Research Track, 84–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86520-7_6.

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Li, Jinxing, Bob Zhang, and David Zhang. "Information Fusion Based on Gaussian Process Latent Variable Model." In Information Fusion, 51–99. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8976-5_3.

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Taubert, Nick, and Martin A. Giese. "Hierarchical Deep Gaussian Processes Latent Variable Model via Expectation Propagation." In Lecture Notes in Computer Science, 317–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86365-4_26.

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Souriau, Rémi, Vincent Vigneron, Jean Lerbet, and Hsin Chen. "Probit Latent Variables Estimation for a Gaussian Process Classifier: Application to the Detection of High-Voltage Spindles." In Latent Variable Analysis and Signal Separation, 514–23. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93764-9_47.

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Eleftheriadis, Stefanos, Ognjen Rudovic, and Maja Pantic. "Shared Gaussian Process Latent Variable Model for Multi-view Facial Expression Recognition." In Advances in Visual Computing, 527–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41914-0_52.

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Conference papers on the topic "Processus gaussiens latents"

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Yang, Liu, Cassandra Heiselman, J. Gerald Quirk, and Petar M. Djuric. "Class-Imbalanced Classifiers Using Ensembles of Gaussian Processes And Gaussian Process Latent Variable Models." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414754.

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Lawrence, Neil D., and Andrew J. Moore. "Hierarchical Gaussian process latent variable models." In the 24th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273557.

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Chen, Kai, Twan van Laarhoven, Elena Marchiori, Feng Yin, and Shuguang Cui. "Multitask Gaussian Process With Hierarchical Latent Interactions." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9746570.

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PFINGSTL, SIMON, CHRISTIAN BRAUN, and MARKUS ZIMMERMANN. "WARPED GAUSSIAN PROCESSES FOR PROGNOSTIC HEALTH MONITORING." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36358.

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Abstract:
Gaussian process regression is a powerful method for predicting states associated with uncertainty. A common application field is to predict damage states of structural systems. Recently, Gaussian processes became very popular as they deliver credible intervals for the predicted states. However, one major disadvantage of Gaussian processes is that they assume a normal distribution. This is not justified when the relevant variables can only assume positive values, such as crack lengths or damage states. This paper presents a way to bypass this problem by using warped Gaussian processes: We (1) transform the data with a warping function, (2) apply Gaussian process regression in the latent space, and (3) transform the results back by using the inverse of the warping function. The method is applied to a crack growth example. The paper shows how to integrate prior knowledge into warped Gaussian processes in order to increase prediction accuracy and that warped Gaussian processes lead to better and more plausible results.
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Su, Chang, and Sargur Srihari. "Latent Fingerprint Core Point Prediction Based on Gaussian Processes." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.404.

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Li, Shibo, Wei Xing, Robert M. Kirby, and Shandian Zhe. "Scalable Gaussian Process Regression Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/340.

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Gaussian process regression networks (GPRN) are powerful Bayesian models for multi-output regression, but their inference is intractable. To address this issue, existing methods use a fully factorized structure (or a mixture of such structures) over all the outputs and latent functions for posterior approximation, which, however, can miss the strong posterior dependencies among the latent variables and hurt the inference quality. In addition, the updates of the variational parameters are inefficient and can be prohibitively expensive for a large number of outputs. To overcome these limitations, we propose a scalable variational inference algorithm for GPRN, which not only captures the abundant posterior dependencies but also is much more efficient for massive outputs. We tensorize the output space and introduce tensor/matrix-normal variational posteriors to capture the posterior correlations and to reduce the parameters. We jointly optimize all the parameters and exploit the inherent Kronecker product structure in the variational model evidence lower bound to accelerate the computation. We demonstrate the advantages of our method in several real-world applications.
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Zhang, Jiayuan, Ziqi Zhu, and Jixin Zou. "Supervised Gaussian process latent variable model based on Gaussian mixture model." In 2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC). IEEE, 2017. http://dx.doi.org/10.1109/spac.2017.8304262.

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Liu, Yuhao, and Petar M. Djuric. "Tracking the Dimensions of Latent Spaces of Gaussian Process Latent Variable Models." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9746538.

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Song, Guoli, Shuhui Wang, Qingming Huang, and Qi Tian. "Multimodal Gaussian Process Latent Variable Models with Harmonization." In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017. http://dx.doi.org/10.1109/iccv.2017.538.

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Eciolaza, Luka, M. Alkarouri, N. D. Lawrence, V. Kadirkamanathan, and P. J. Fleming. "Gaussian Process Latent Variable Models for Fault Detection." In 2007 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2007. http://dx.doi.org/10.1109/cidm.2007.368886.

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