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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
551

Parcimonie dans les modèles Markoviens et application à l'analyse des séquences biologiques / Parsimonious Markov models and application to biological sequence analysis

Bourguignon, Pierre Yves Vincent 15 December 2008 (has links)
Les chaînes de Markov constituent une famille de modèle statistique incontournable dans de nombreuses applications, dont le spectre s'étend de la compression de texte à l'analyse des séquences biologiques. Un problème récurrent dans leur mise en oeuvre face à des données réelles est la nécessité de compromettre l'ordre du modèle, qui conditionne la complexité des interactions modélisées, avec la quantité d'information fournies par les données, dont la limitation impacte négativement la qualité des estimations menées. Les arbres de contexte permettent une granularité fine dans l'établissement de ce compromis, en permettant de recourir à des longueurs de mémoire variables selon le contexte rencontré dans la séquence. Ils ont donné lieu à des outils populaires tant pour l'indexation des textes que pour leur compression (Context Tree Maximisation – CTM - et Context Tree Weighting - CTW). Nous proposons une extension de cette classe de modèles, en introduisant les arbres de contexte parcimonieux, obtenus par fusion de noeuds issus du même parent dans l'arbre. Ces fusions permettent une augmentation radicale de la granularité de la sélection de modèle, permettant ainsi de meilleurs compromis entre complexité du modèle et qualité de l'estimation, au prix d'une extension importante de la quantité de modèles mise en concurrence. Cependant, grâce à une approche bayésienne très similaire à celle employée dans CTM et CTW, nous avons pu concevoir une méthode de sélection de modèles optimisant de manière exacte le critère bayésien de sélection de modèles tout en bénéficiant d'une programmation dynamique. Il en résulte un algorithme atteignant la borne inférieure de la complexité du problème d'optimisation, et pratiquement tractable pour des alphabets de taille inférieure à 10 symboles. Diverses démonstrations de la performance atteinte par cette procédure sont fournies en dernière partie. / Markov chains, as a universal model accounting for finite memory, discrete valued processes, are omnipresent in applied statistics. Their applications range from text compression to the analysis of biological sequences. Their practical use with finite samples, however, systematically require to draw a compromise between the memory length of the model used, which conditions the complexity of the interactions the model may capture, and the amount of information carried by the data, whose limitation negatively impacts the quality of estimation. Context trees, as an extension of the model class of Markov chains, provide the modeller with a finer granularity in this model selection process, by allowing the memory length to vary across contexts. Several popular modelling methods are based on this class of models, in fields such as text indexation of text compression (Context Tree Maximization and Context Tree Weighting). We propose an extension of the models class of context trees, the Parcimonious context trees, which further allow the fusion of sibling nodes in the context tree. They provide the modeller with a yet finer granularity to perform the model selection task, at the cost of an increased computational cost for performing it. Thanks to a bayesian approach of this problem borrowed from compression techniques, we succeeded at desiging an algorithm that exactly optimizes the bayesian criterion, while it benefits from a dynamic programming scheme ensuring the minimisation of the computational complexity of the model selection task. This algorithm is able to perform in reasonable space and time on alphabets up to size 10, and has been applied on diverse datasets to establish the good performances achieved by this approach.
552

Etude de la variabilité hémodynamique chez l’enfant et l’adulte sains en IRMf / Study of hemodynamic variability in sane adult and children in fMRI

Badillo, Solveig 18 November 2013 (has links)
En IRMf, les conclusions de paradigmes expérimentaux restent encore sujettes à caution dans la mesure où elles supposent une connaissance a priori du couplage neuro-vasculaire, c’est-à- dire de la fonction de réponse hémodynamique qui modélise le lien entre la stimulation et le signal mesuré. Afin de mieux appréhender les changements neuronaux et vasculaires induits par la réalisation d’une tâche cognitive en IRMf, il apparaît donc indispensable d’étudier de manière approfondie les caractéristiques de la réponse hémodynamique. Cette thèse apporte un nouvel éclairage sur cette étude, en s’appuyant sur une méthode originale d’analyse intra-sujet des données d’IRMf : la Détection-Estimation Conjointe (« Joint Detection-Estimation » en anglais, ou JDE). L’approche JDE modélise de façon non paramétrique et multivariée la réponse hémodynamique, tout en détectant conjointement les aires cérébrales activées en réponse aux stimulations d’un paradigme expérimental. La première contribution de cette thèse a été centrée sur l’analyse approfondie de la variabilité hémodynamique, tant inter-individuelle qu’inter-régionale, au niveau d’un groupe de jeunes adultes sains. Ce travail a permis de valider la méthode JDE au niveau d’une population et de mettre en évidence la variabilité hémodynamique importante apparaissant dans certaines régions cérébrales : lobes pariétal, temporal, occipital, cortex moteur. Cette variabilité est d’autant plus importante que la région est impliquée dans des processus cognitifs plus complexes.Un deuxième axe de recherche a consisté à se focaliser sur l’étude de l’organisation hémodynamique d’une aire cérébrale particulièrement importante chez les êtres humains, la région du langage. Cette fonction étant liée à la capacité d’apprentissage de la lecture, deux groupes d’enfants sains, âgés respectivement de 6 et 9 ans, en cours d’apprentissage ou de consolidation de la lecture, ont été choisis pour mener cette étude. Deux apports méthodologiques importants ont été proposés. Tout d’abord, une extension multi-sessions de l’approche JDE (jusqu’alors limitée au traitement de données mono-session en IRMf) a été mise au point afin d’améliorer la robustesse et la reproductibilité des résultats. Cette extension a permis de mettre en évidence, au sein de la population d’enfants, l’évolution de la réponse hémodynamique avec l’âge, au sein de la région du sillon temporal supérieur. Ensuite, un nouveau cadre a été développé pour contourner l’une des limitations de l’approche JDE « standard », à savoir la parcellisation a priori des données en régions fonctionnellement homogènes. Cette parcellisation est déterminante pour la suite de l’analyse et a un impact sur les résultats hémodynamiques. Afin de s’affranchir d’un tel choix, l’alternative mise au point combine les résultats issus de différentes parcellisations aléatoires des données en utilisant des techniques de «consensus clustering». Enfin, une deuxième extension de l’approche JDE a été mise en place pour estimer la forme de la réponse hémodynamique au niveau d’un groupe de sujets. Ce modèle a pour l’instant été validé sur simulations, et nous prévoyons de l’appliquer sur les données d’enfant pour améliorer l’étude des caractéristiques temporelles de la réponse BOLD dans les réseaux du langage.Ce travail de thèse propose ainsi d’une part des contributions méthodologiques nouvelles pour caractériser la réponse hémodynamique en IRMf, et d’autre part une validation et une application des approches développées sous un éclairage neuroscientifique. / In fMRI, the conclusions of experimental paradigms remain unreliable as far as they supposesome a priori knowledge on the neuro-vascular coupling which is characterized by thehemodynamic response function modeling the link between the stimulus input and the fMRIsignal as output. To improve our understanding of the neuronal and vascular changes inducedby the realization of a cognitive task given in fMRI, it seems thus critical to study thecharacteristics of the hemodynamic response in depth.This thesis gives a new perspective on this topic, supported by an original method for intra-subjectanalysis of fMRI data : the Joint Detection-Estimation (or JDE). The JDE approachmodels the hemodynamic response in a not parametric and multivariate manner, while itjointly detects the cerebral areas which are activated in response to stimulations deliveredalong an experimental paradigm.The first contribution of this thesis is centered on the thorough analysis of the interindividualand inter-regiona hemodynamic variability from a population of young healthyadults. This work has allowed to validate the JDE method at the group level and to highlightthe striking hemodynamic variability in some cerebral regions : parietal, temporal, occipitallobes, motor cortex. This variability is much more important as the region is involved in morecomplex cognitive processes.The second research axis has consisted in focusing on the study of the hemodynamic orga-nizationof a particularly important cerebral area in Humans, the language system. Becausethis function embeds the reading learning ability, groups of healthy children of 6 and 9 yearsold respectively, who were in the process of learning or of strenghting reading, were chosen forthis study. Two important methodological contributions have been proposed. First, a multi-sessionsextension of the JDE approach (until now limited to the processing of mono-sessiondata in fMRI) was worked out in order to improve the robustness and the reproducibility ofthe results. Then, a new framework was developed to overcome the main shortcoming of theJDE approach. The latter indeed relies on a prior parcellation of the data in functionally ho-mogeneousregions, the choice of which is critical for the subsequent inference and impacts thehemodynamic results. In order to avoid this a priori choice, the finalized alternative combinesthe results from various random data fragmentations by using “consensus clustering”.Finally, a second extension of the JDE approach was developed in order to robustly estimatethe shape of the hemodynamic response at the group level. So far, this model was validatedon simulations, and we plan to apply it on children data to improve the study of the BOLDresponse temporal characteristics in the language areas. Thus, this PhD work proposes onone hand new methodological contributions to characterize the hemodynamic response infMRI, and on the other hand a validation and a neuroscientific application of the proposedapproaches.
553

Some developments of local quasi-likelihood estimation and optimal Bayesian sampling plans for censored data. / CUHK electronic theses & dissertations collection / Digital dissertation consortium

January 1999 (has links)
by Jian Wei Chen. / "May 1999." / Thesis (Ph.D.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (p. 178-180). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.
554

Ensaios sobre macroeconometria bayesiana aplicada / Essays on bayesian macroeconometrics

Santos, Fernando Genta dos 03 February 2012 (has links)
Os três artigos que compõe esta Tese possuem em comum a utilização de técnicas macroeconométricas bayesianas, aplicadas a modelos dinâmicos e estocásticos de equilíbrio geral, para a investigação de problemas específicos. Desta forma, esta Tese busca preencher importantes lacunas presentes na literatura nacional e internacional. No primeiro artigo, estimou-se a importância do canal de custo da política monetária por meio de um modelo novo-keynesiano dinâmico e estocástico de equilíbrio geral. Para tanto, alteramos o modelo convencional, assumindo que uma parcela das firmas precise contrair empréstimos para pagar sua folha salarial. Desta forma, a elevação da taxa nominal de juro impacta positivamente o custo unitário do trabalho efetivo, podendo acarretar em aumento da inflação. Este artigo analisa as condições necessárias para que o modelo gere esta resposta positiva da inflação ao aperto monetário, fenômeno esse que ficou conhecido como price puzzle. Devido ao uso da metodologia DSGE-VAR, os resultados aqui encontrados podem ser comparados tanto com a literatura que trata o puzzle como um problema de identificação dos modelos VAR como com a literatura que avalia o canal de custo por meio de modelos novo-keynesianos. No segundo artigo, avaliamos até que ponto as expectativas de inflação geradas por um modelo dinâmico e estocástico de equilíbrio geral são compatíveis com as expectativas coletadas pelo Banco Central do Brasil (BCB). Este procedimento nos permite analisar a racionalidade das expectativas dos agentes econômicos brasileiros, comparando-as não à inflação observada, mas sim à projeção de um modelo desenvolvido com a hipótese de expectativas racionais. Além disso, analisamos os impactos do uso das expectativas coletadas pelo BCB na estimação do nosso modelo, no que se refere aos parâmetros estruturais, função de resposta ao impulso e análise de decomposição da variância. Por fim, no terceiro artigo desta Tese, modificamos o modelo novo-keynesiano convencional, de forma a incluir a teoria do desemprego proposta pelo economista Jordi Galí. Com isso, procuramos preencher uma lacuna importante na literatura nacional, dominada por modelos que não contemplam a possibilidade de desequilíbrios no mercado de trabalho capazes de gerar desemprego involuntário. A interpretação alternativa do mercado de trabalho aqui utilizada permite superar os problemas de identificação notoriamente presentes na literatura, tornando o modelo resultante mais robusto. Desta forma, utilizamos o modelo resultante para, dentre outras coisas, avaliar os determinantes da taxa de desemprego ao longo da última década. / The three articles that comprise this thesis have in common the use of macroeconometric bayesian techniques, applied to dynamic stochastic general equilibrium models, for the investigation of specific problems. Thus, this thesis seeks to fill important gaps present in the national and international literatures. In the first article, I estimated the importance of the cost-push channel of monetary policy through a new keynesian dynamic stochastic general equilibrium model. To this end, we changed the conventional model, assuming now that a share of firms needs to borrow to pay its payroll. Thus, an increase in the nominal interest rate positively impacts the effective unit labor cost and may result in an inflation hike. This article analyzes the necessary conditions for the model to exhibit a positive response of inflation to a monetary tightening, a phenomenon that became known as the price puzzle. Because I use the DSGE-VAR methodology, the present results can be compared both with the empirical literature dealing with the puzzle as an identification problem of VAR models and with the theoretical literature that evaluates the cost-push channel through new keynesian models. In the second article, we assess the extent to which inflation expectations generated by a dynamic stochastic general equilibrium model are consistent with expectations compiled by the Central Bank of Brazil (BCB). This procedure allows us to analyze the rationality of economic agents\' expectations in Brazil, comparing them not with the observed inflation, but with the forecasts of a model developed with the hypothesis of rational expectations. In addition, we analyze the impacts of using expectations compiled by the BCB in the estimation of our model, looking at the structural parameters, the impulse response function and variance decomposition analysis. Finally, the third article in this thesis, I modified the conventional new keynesian model, to include unemployment as proposed by the economist Jordi Galí. With that, I fill an important gap in the national literature, dominated by models that do not contemplate the possibility of disequilibrium in the labor market that can generate involuntary unemployment. The alternative interpretation of the labor market used here overcomes the identification problems notoriously present in the literature, making the resulting model more robust to the Lucas critique. Thus, I use the resulting model to assess the determinants of the unemployment rate over the last decade, among other points.
555

Topics in Computational Bayesian Statistics With Applications to Hierarchical Models in Astronomy and Sociology

Sahai, Swupnil January 2018 (has links)
This thesis includes three parts. The overarching theme is how to analyze structured hierarchical data, with applications to astronomy and sociology. The first part discusses how expectation propagation can be used to parallelize the computation when fitting big hierarchical bayesian models. This methodology is then used to fit a novel, nonlinear mixture model to ultraviolet radiation from various regions of the observable universe. The second part discusses how the Stan probabilistic programming language can be used to numerically integrate terms in a hierarchical bayesian model. This technique is demonstrated on supernovae data to significantly speed up convergence to the posterior distribution compared to a previous study that used a Gibbs-type sampler. The third part builds a formal latent kernel representation for aggregate relational data as a way to more robustly estimate the mixing characteristics of agents in a network. In particular, the framework is applied to sociology surveys to estimate, as a function of ego age, the age and sex composition of the personal networks of individuals in the United States.
556

Selected Legal Applications for Bayesian Methods

Cheng, Edward K. January 2018 (has links)
This dissertation offers three contexts in which Bayesian methods can address tricky problems in the legal system. Chapter 1 offers a method for attacking case publication bias, the possibility that certain legal outcomes may be more likely to be published or observed than others. It builds on ideas from multiple systems estimation (MSE), a technique traditionally used for estimating hidden populations, to detect and correct case publication bias. Chapter 2 proposes new methods for dividing attorneys' fees in complex litigation involving multiple firms. It investigates optimization and statistical approaches that use peer reports of each firm's relative contribution to estimate a "fair" or consensus division of the fees. The methods proposed have lower informational requirements than previous work and appear to be robust to collusive behavior by the firms. Chapter 3 introduces a statistical method for classifying legal cases by doctrinal area or subject matter. It proposes using a latent space approach based on case citations as an alternative to the traditional manual coding of cases, reducing subjectivity, arbitrariness, and confirmation bias in the classification process.
557

A Bayesian Approach to the Understanding of Exoplanet Populations and the Origin of Life

Chen, Jingjing January 2018 (has links)
The study of extrasolar planets, or exoplanets for short, has developed rapidly over the last decade. While we have spent much effort building both ground-based and space telescopes to search for exoplanets, it is even more important that we use the observational data wisely to understand them. Exoplanets are of great interest to both astronomers and the general public because they have shown varieties of characteristics that we couldn't have anticipated from planets within our Solar System. To properly analyze the exoplanet populations, we need the tools of statistics. Therefore, in Chapter 1, I describe the science background as well as the statistical methods which will be applied in this thesis. In Chapter 2, I discuss how to train a hierarchical Bayesian model in detail to fit the relationship between masses and radii of exoplanets and categorize exoplanets based on that. A natural application that comes with the model is to use it for future observations of mass/radius and predict the other measurement. Thus I will show two application cases in Chapter 3. Composition of an exoplanet is also very much constrained by its mass and radius. I will show an easy way to constrain the composition of exoplanets in Chapter 4 and discuss how more complicated methods can be applied in future works. Of even greater interest is whether there is life elsewhere in the Universe. Although the future discovery of extraterrestrial life might be totally a fluke, a clear sketched plan always gives us some directions. Research in this area is still very preliminary. Fortunately, besides directly searching for extraterrestrial life, we can also apply statistical reasoning to first estimate the rate of abiogenesis, which will give us some clue on the question of whether there is extraterrestrial life in a probabilistic way. In Chapter 5, I will discuss how different methods can constrain the abiogenesis rate in an informatics perspective. Finally I will give a brief summary in Chapter 6.
558

Bayesian predictive models of user intention

Mestre, María del Rosario January 2015 (has links)
No description available.
559

Bayesian time series learning with Gaussian processes

Frigola-Alcalde, Roger January 2016 (has links)
No description available.
560

Bayesian variable selection for high dimensional data analysis. / CUHK electronic theses & dissertations collection

January 2010 (has links)
In the practice of statistical modeling, it is often desirable to have an accurate predictive model. Modern data sets usually have a large number of predictors. For example, DNA microarray gene expression data usually have the characteristics of fewer observations and larger number of variables. Hence parsimony is especially an important issue. Best-subset selection is a conventional method of variable selection. Due to the large number of variables with relatively small sample size and severe collinearity among the variables, standard statistical methods for selecting relevant variables often face difficulties. / In the third part of the thesis, we propose a Bayesian stochastic search variable selection approach for multi-class classification, which can identify relevant genes by assessing sets of genes jointly. We consider a multinomial probit model with a generalized g-prior for the regression coefficients. An efficient algorithm using simulation-based MCMC methods are developed for simulating parameters from the posterior distribution. This algorithm is robust to the choice of initial value, and produces posterior probabilities of relevant genes for biological interpretation. We demonstrate the performance of the approach with two well- known gene expression profiling data: leukemia data and lymphoma data. Compared with other classification approaches, our approach selects smaller numbers of relevant genes and obtains competitive classification accuracy based on obtained results. / The last part of the thesis is about the further research, which presents a stochastic variable selection approach with different two-level hierarchical prior distributions. These priors can be used as a sparsity-enforcing mechanism to perform gene selection for classification. Using simulation-based MCMC methods for simulating parameters from the posterior distribution, an efficient algorithm can be developed and implemented. / The second part of the thesis proposes a Bayesian stochastic variable selection approach for gene selection based on a probit regression model with a generalized singular g-prior distribution for regression coefficients. Using simulation-based MCMC methods for simulating parameters from the posterior distribution, an efficient and dependable algorithm is implemented. It is also shown that this algorithm is robust to the choice of initial values, and produces posterior probabilities of related genes for biological interpretation. The performance of the proposed approach is compared with other popular methods in gene selection and classification via the well known colon cancer and leukemia data sets in microarray literature. / Yang, Aijun. / Adviser: Xin-Yuan Song. / Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 89-98). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

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