La mesure de la hauteur des océans, des vents de surface (fortement liés aux températures des océans), ou encore de la hauteur des vagues sont un ensemble de paramètres nécessaires à l'étude des océans mais aussi au suivi de leurs évolutions : l'altimétrie spatiale est l'une des disciplines le permettant. Une forme d'onde altimétrique est le résultat de l'émission d'une onde radar haute fréquence sur une surface donnée (classiquement océanique) et de la mesure de la réflexion de cette onde. Il existe actuellement une méthode d'estimation non optimale des formes d'onde altimétriques ainsi que des outils de classifications permettant d'identifier les différents types de surfaces observées. Nous proposons dans cette étude d'appliquer la méthode d'estimation bayésienne aux formes d'onde altimétriques ainsi que de nouvelles approches de classification. Nous proposons enfin la mise en place d'un algorithme spécifique permettant l'étude de la topographie en milieu côtier, étude qui est actuellement très peu développée dans le domaine de l'altimétrie. / After having scanned the ocean levels during thirteen years, the french/american satelliteTopex-Poséidon disappeared in 2005. Topex-Poséidon was replaced by Jason-1 in december 2001 and a new satellit Jason-2 is waited for 2008. Several estimation methods have been developed for signals resulting from these satellites. In particular, estimators of the sea height and wave height have shown very good performance when they are applied on waveforms backscattered from ocean surfaces. However, it is a more challenging problem to extract relevant information from signals backscattered from non-oceanic surfaces such as inland waters, deserts or ices. This PhD thesis is divided into two parts : A first direction consists of developing classification methods for altimetric signals in order to recognize the type of surface affected by the radar waveform. In particular, a specific attention will be devoted to support vector machines (SVMs) and functional data analysis for this problem. The second part of this thesis consists of developing estimation algorithms appropriate to altimetric signals obtained after reflexion on non-oceanic surfaces. Bayesian algorithms are currently under investigation for this estimation problem. This PhD is co-supervised by the french society CLS (Collect Localisation Satellite) (seehttp://www.cls.fr/ for more details) which will in particular provide the real altimetric data necessary for this study.

<p>Environmental sensing is experiencing tremendous development due largely to the advancement of sensor technology and wireless technology/internet that connects them and enable data exchange. Environmental monitoring sensor systems range from satellites that continuously monitor earth surface to miniature wearable devices that track local environment and people's activities. However, transforming these data into knowledge of the underlying physical and/or chemical processes remains a big challenge given the spatial, temporal scale, and heterogeneity of the relevant natural phenomena. This research focuses on the development and application of novel sensing and inference techniques in air and water environments. The overall goal is to infer the state and dynamics of some key environmental variables by building various models: either a sensor system or numerical simulations that capture the physical processes.</p><p>This dissertation is divided into five chapters. Chapter 1 introduces the background and motivation of this research. Chapter 2 focuses on the evaluation of different models (physically-based versus empirical) and remote sensing data (multispectral versus hyperspectral) for suspended sediment concentration (SSC) retrieval in shallow water environments. The study site is the Venice lagoon (Italy), where we compare the estimated SSC from various models and datasets against in situ probe measurements. The results showed that the physically-based model provides more robust estimate of SSC compared against empirical models when evaluated using the cross-validation method (leave-one-out). Despite the finer spectral resolution and the choice of optimal combinations of bands, the hyperspectral data is less reliable for SSC retrieval comparing to multispectral data due to its limited amount of historical dataset, information redundancy, and cross-band correlation.</p><p>Chapter 3 introduces a multipollutant sensor/sampler system that developed for use on mobile applications including aerostats and unmanned aerial vehicles (UAVs). The system is particularly applicable to open area sources such as forest fires, due to its light weight (3.5 kg), compact size (6.75 L), and internal power supply. The sensor system, termed “Kolibri”, consists of low-cost sensors measuring CO2 and CO, and samplers for particulate matter and volatile organic compounds (VOCs). The Kolibri is controlled by a microcontroller, which can record and transfer data in real time using a radio module. Selection of the sensors was based on laboratory testing for accuracy, response delay and recovery, cross-sensitivity, and precision. The Kolibri was compared against rack-mounted continuous emission monitors (CEMs) and another mobile sampling instrument (the ``Flyer'') that had been used in over ten open area pollutant sampling events. Our results showed that the time series of CO, CO2, and PM2.5 concentrations measured by the Kolibri agreed well with those from the CEMs and the Flyer. The VOC emission factors obtained using the Kolibri are comparable to existing literature values. The Kolibri system can be applied to various open area sampling challenging situations such as fires, lagoons, flares, and landfills.</p><p>Chapter 4 evaluates the trade-off between sensor quality and quantity for fenceline monitoring of fugitive emissions. This research is motivated by the new air quality standard that requires continuous monitoring of hazardous air pollutants (HAPs) along the fenceline of oil and gas refineries. Recently, the emergence of low-cost sensors enables the implementation of spatially-dense sensor network that can potentially compensate for the low quality of individual sensors. To quantify sensor inaccuracy and uncertainty of describing gas concentration that is governed by turbulent air flow, a Bayesian approach is applied to probabilistically infer the leak source and strength. Our results show that a dense sensor network can partly compensate for low-sensitivity or high noise of individual sensors. However, the fenceline monitoring approach fails to make an accurate leak detection when sensor/wind bias exists even with a dense sensor network.</p><p>Chapter 5 explores the feasibility of applying a mobile sensing approach to estimate fugitive methane emissions in suburban and rural environments. We first compare the mobile approach against a stationary method (OTM33A) proposed by the US EPA using a series of controlled release tests. Analysis shows that the mobile sensing approach can reduce estimation bias and uncertainty compared against the OTM33A method. Then, we apply this mobile sensing approach to quantify fugitive emissions from several ammonia fertilizer plants in rural areas. Significant methane emission was identified from one plant while the other two shows relatively low emissions. Sensitivity analysis of several model parameters shows that the error term in the Bayesian inference is vital for the determination of model uncertainty while others are less influential. Overall, this mobile sensing approach shows promising results for future applications of quantifying fugitive methane emission in suburban and rural environments.</p> / Dissertation

Attention allows us to monitor objects or regions of visual space and extract information from them to use for report or storage. Classical theories of attention assumed a single focus of selection but many everyday activities, such as playing video games, suggest otherwise. Nonetheless, the underlying mechanism which can explain the ability to divide attention has not been well established. Numerous attempts have been made in order to clarify divided attention, including analytical strategies as well as methods working with visual phenomena, even more sophisticated predictors incorporating information about past selection decisions. Virtually all the attempts approach this problem by constructing a simplified model of attention. In this study, we develop a version of the existing Bayesian framework to propose such models, and evaluate their ability to generate eye movement trajectories. For the comparison of models, we use the eye movement trajectories generated by several analytical strategies. We measure the similarity between...

Attention allows us to monitor objects or regions of visual space and extract information from them to use for report or storage. Classical theories of attention assumed a single focus of selection but many everyday activities, such as playing video games, suggest otherwise. Nonetheless, the underlying mechanism which can explain the ability to divide attention has not been well established. Numerous attempts have been made in order to clarify divided attention, including analytical strategies as well as methods working with visual phenomena, even more sophisticated predictors incorporating information about past selection decisions. Virtually all the attempts approach this problem by constructing a simplified model of attention. In this study, we develop a version of the existing Bayesian framework to propose such models, and evaluate their ability to generate eye movement trajectories. For the comparison of models, we use the eye movement trajectories generated by several analytical strategies. We measure the...

This work employs statistical and Bayesian techniques to analyze mathematical forward models with several sources of uncertainty. The forward models usually arise from phenomenological and physical phenomena and are expressed through regression-based models or partial differential equations (PDEs) associated with uncertain parameters and input data. One of the critical challenges in real-world applications is to quantify uncertainties of the unknown parameters using observations. To this purpose, methods based on the likelihood function, and Bayesian techniques constitute the two main statistical inferential approaches considered here. Two problems are studied in this thesis. The first problem is the prediction of fatigue life of metallic specimens. The second part is related to inverse problems in linear PDEs. Both problems require the inference of unknown parameters given certain measurements. We first estimate the parameters by means of the maximum likelihood approach. Next, we seek a more comprehensive Bayesian inference using analytical asymptotic approximations or computational techniques. In the fatigue life prediction, there are several plausible probabilistic stress-lifetime (S-N) models. These models are calibrated given uniaxial fatigue experiments. To generate accurate fatigue life predictions, competing S-N models are ranked according to several classical information-based measures. A different set of predictive information criteria is then used to compare the candidate Bayesian models. Moreover, we propose a spatial stochastic model to generalize S-N models to fatigue crack initiation in general geometries. The model is based on a spatial Poisson process with an intensity function that combines the S-N curves with an averaged effective stress that is computed from the solution of the linear elasticity equations.

<div>The multi-armed bandit (MAB) problem refers to the task of sequentially assigning treatments to experimental units so as to identify the best treatment(s) while controlling the opportunity cost of further investigation. Many algorithms have been developed that attempt to balance this trade-off between exploiting the seemingly optimum treatment and exploring the other treatments. The selection of an MAB algorithm for implementation in a particular context is often performed by comparing candidate algorithms in terms of their abilities to control the expected regret of exploration versus exploitation. This singular criterion of mean regret is insufficient for many practical problems, and therefore an additional criterion that should be considered is control of the variance, or risk, of regret.</div><div>This work provides an overview of how the existing prominent MAB algorithms handle both criteria. We additionally investigate the effects of incorporating prior information into an algorithm's model, including how sharing information across treatments affects the mean and variance of regret.</div><div>A unified and accessible framework does not currently exist for constructing MAB algorithms that control both of these criteria. To this end, we develop such a framework based on the two elementary concepts of dismemberment of treatments and a designed learning phase prior to dismemberment. These concepts can be incorporated into existing MAB algorithms to effectively yield new algorithms that better control the expectation and variance of regret. We demonstrate the utility of our framework by constructing new variants of the Thompson sampler that involve a small number of simple tuning parameters. As we illustrate in simulation and case studies, these new algorithms are implemented in a straightforward manner and achieve improved control of both regret criteria compared to the traditional Thompson sampler. Ultimately, our consideration of additional criteria besides expected regret illuminates novel insights into the multi-armed bandit problem.</div><div>Finally, we present visualization methods, and a corresponding R Shiny app for their practical execution, that can yield insights into the comparative performances of popular MAB algorithms. Our visualizations illuminate the frequentist dynamics of these algorithms in terms of how they perform the exploration-exploitation trade-off over their populations of realizations as well as the algorithms' relative regret behaviors. The constructions of our visualizations facilitate a straightforward understanding of complicated MAB algorithms, so that our visualizations and app can serve as unique and interesting pedagogical tools for students and instructors of experimental design.</div>

Orientador: Humberto Tonhati / Coorientador: Rusbel Raúl Aspilcueta Borquis / Banca: João Ademir de Oliveira / Banca: Anibal Eugênio Vercesi Filho / Resumo: Este trabalho teve como objetivo estimar parâmetros genéticos para características de crescimento, utilizando-se um modelo multi-característica. Foram analisados registros de 14956 animais da raça Brahman, participantes do programa de melhoramento da raça Brahman, desenvolvido pela Associação Nacional de Criadores e Pesquisadores (ANCP). Por meio de inferência bayesiana foram obtidas estimativas de componentes de variância para os pesos nas idades padrão aos 60 (P60), 120 (P120), 210 (P210), 365 (P365), 450 (P450) e 550 (P550) dias. As análises foram realizadas empregando-se o software GIBBS2F90, assumindo um modelo animal. Para os pesos às idades padronizadas estudadas foram considerados os efeitos fixos de grupo de contemporâneos (rebanho - ano nascimento - estação de nascimento - sexo - manejo) e idade do animal no momento da pesagem (linear e quadrático) como covariável, e os efeitos aleatórios genético aditivo direto, genético aditivo materno e residual. As estimativas de herdabilidade genética direta foram 0,31 (P60), 0,37 (P120), 0,34 (P210), 0,38 (P365), 0,37 (P450) e 0,45 (P550). As estimativas de herdabilidade genética materna foram 0,18 (P60), 0,19 (P120), 0,22 (P210), 0,14 (P365), 0,11 (P450), e 0,08 (P550). Os valores de correlação genética direta variaram de 0,79 (P60 / P450) a 0,94 (P365 / P450). Em vista dos parâmetros estimados, verifica-se que as características aqui estudadas apresentam variabilidade genética suficiente para realizar a seleção dos animais. As correlações genéticas indicam que a seleção simultânea para as características em estudo pode ser eficiente / Abstract: The objective of this work was to estimate genetic parameters for growth traits using a multiple-traits model. Were analyzed records of 14956 animals of Brahman Breed, participants of Breeding Program of Brahman cattle, developed by Breeders and Researchers National Association (ANCP). Using Bayesian inference were obtained estimations of variance components for standardized weights at 60 (W60), 120 (W120), 210 (W210), 365 (W365), 450 (W450) and 550 (W550) days. Analyzes were done using Gibbs2f90 Software, assuming an animal model. For standardized weights were considered fixed effects of contemporary group (herd - birth year - birth season - sex - management) and animal age at weighting moment (linear and quadratic) as covariate, and randomized effects of direct additive genetic, maternal additive genetic and residual. Direct Heritability estimations were 0,31 (W60), 0,37 (W120), 0,34 (W210), 0,38 (W365), 0,37 (W450) and 0,45 (W550). Maternal heritability estimations were 0,18 (W60), 0,19 (W120), 0,22 (W210), 0,14 (W365), 0,11 (W450), and 0,08 (W550). Genetic correlation values varied from 0,79 (W60 / W450) a 0,94 (W365 / W450).. According estimated genetic parameters, was verified that standardized weights presented enough genetic variation for animal selection. Genetic correlations indicate that a simultaneously selection for all the traits of this study could be efficient / Mestre

A inferência Bayesiana está cada vez mais dependente de algoritmos de simulação estocástica, e sua eficiência está diretamente relacionada à eficiência do algoritmo considerado. Uma prática bastante utilizada é a introdução de variáveis auxiliares para obtenção de formas conhecidas para as distribuições {\\it a posteriori} condicionais completas, as quais facilitam a implementação do amostrador de Gibbs. No entanto, a introdução dessas variáveis pode produzir algoritmos onde os valores simulados são fortemente correlacionados, fato esse que prejudica a convergência. O agrupamento das quantidades desconhecidas em blocos, de tal maneira que seja viável a simulação conjunta destas quantidades, é uma alternativa para redução da autocorrelação, e portanto, ajuda a melhorar a eficiência do procedimento de simulação. Neste trabalho, apresentamos propostas de simulação em blocos no contexto de modelos de regressão binária com o uso de variáveis auxiliares. Três classes de funções de ligação são consideradas: probito, logito e probito-assimétrico. Para as duas primeiras apresentamos e implementamos as propostas de atualização conjunta feitas por Holmes e Held (2006). Para a ligação probito-assimétrico propomos quatro diferentes maneiras de construir os blocos, e comparamos estes algoritmos através de duas medidas de eficiência (distância média Euclidiana entre atualizações e tamanho efetivo da amostra). Concluímos que os algoritmos propostos são mais eficientes que o convencional (sem blocos), sendo que um deles proporcionou ganho superior a 160\\% no tamanho efetivo da amostra. Além disso, discutimos uma etapa bastante importante da modelagem, denominada análise de resíduos. Nesta parte adaptamos e implementamos os resíduos propostos para a ligação probito para os modelos logístico e probito-assimétrico. Finalmente, utilizamos os resíduos propostos para verificar a presença de observações discrepantes em um conjunto de dados simulados. / The Bayesian inference is getting more and more dependent of stochastic simulation algorithms, and its efficiency is directly related with the efficiency of the considered algorithm. The introduction of auxiliary variables is a technique widely used for attainment of the full conditional distributions, which facilitate the implementation of the Gibbs sampling. However, the introduction of these auxiliary variables can produce algorithms with simulated values highly correlated, this fact harms the convergence. The grouping of the unknow quantities in blocks, in such way that the joint simulation of this quantities is possible, is an alternative for reduction of the autocorrelation, and therefore, improves the efficiency of the simulation procedure. In this work, we present proposals of simulation using the Gibbs block sampler in the context of binary response regression models using auxiliary variables. Three class of links are considered: probit, logit and skew-probit. For the two first we present and implement the scheme of joint update proposed by Holmes and Held (2006). For the skew-probit, we consider four different ways to construct the blocks, and compare these algorithms through two measures of efficiency (the average Euclidean update distance between interactions and effective sample size). We conclude that the considered algorithms are more efficient than the conventional (without blocks), where one of these leading to around 160\\% improvement in the effective sample size. Moreover, we discuss one important stage of the modelling, called residual analysis. In this part we adapt and implement residuals considered in the probit model for the logistic and skew-probit models. For a simulated data set we detect the presence of outlier used the residuals proposed here for the different models.

Introdução: A maior causa de mortes no mundo é devido ao câncer, cerca de 8,2 milhões em 2012 (World Cancer Report, 2014). O câncer de mama é a forma mais comum de câncer entre as mulheres e a segunda neoplasia mais frequente, seguida do câncer de pele não melanoma, representando cerca de 25% de todos os tipos de cânceres diagnosticados. Modelos estatísticos de análise sobrevivência podem ser úteis para a identificação e compreensão de fatores de risco, fatores de prognóstico, bem como na comparação de tratamentos. Métodos: Modelos estatísticos de análise de sobrevivência foram utilizados para evidenciar fatores que afetam os tempos de sobrevida livre da doença e total de um estudo retrospectivo realizado no Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Ribeirão Preto, referente a 54 pacientes com câncer de mama localmente avançado com superexpressão do Her-2 que iniciaram a quimioterapia neoadjuvante associada com o medicamento Herceptin® (Trastuzumabe) no período de 2008 a 2012. Utilizaram-se modelos univariados com distribuição Weibull sem e com a presença de fração de cura sob o enfoque frequentista e bayesiano. Utilizou-se modelos assumindo uma estrutura de dependência entre os tempos observados baseados na distribuição exponencial bivariada de Block Basu, na distribuição geométrica bivariada de Arnold e na distribuição geométrica bivariada de Basu-Dhar. Resultados: Resultados da análise univariada sem a presença de covariáveis, o modelo mais adequado às características dos dados foi o modelo Weibull com a presença de fração de cura sob o enfoque bayesiano. Ao incorporar nos modelos as covariáveis, observou-se melhor ajuste dos modelos com fração de cura, que evidenciaram o estágio da doença como um fator que afeta a sobrevida livre da doença e total. Resultados da análise bivariada sem a presença de covariáveis estimam médias de tempo de sobrevida livre da doença para os modelos Block e Basu, Arnold e Basu-Dhar de 108, 140 e 111 meses, respectivamente e de 232, 343, 296 meses para o tempo de sobrevida total. Ao incorporar as covariáveis, os modelos evidenciam que o estágio da doença afeta a sobrevida livre da doença e total. No modelo de Arnold a covariável tipo de cirurgia também se mostrou significativa. Conclusões: Os resultados do presente estudo apresentam alternativas para a análise de sobrevivência com tempos de sobrevida na presença de fração de cura, censuras e várias covariaveis. O modelo de riscos proporcionais de Cox nem sempre se adequa às características do banco de dados estudado, sendo necessária a busca de modelos estatísticos mais adequados que produzam inferências consistentes. / Introduction: The leading worldwide cause of deaths is due to cancer, about 8.2 million in 2012 (World Cancer Report, 2014). Breast cancer is the most common form of cancer among women and the second most common cancer, followed by non-melanoma skin cancer, accounting for about 25% of all diagnosed types of cancers. Statistical analysis of survival models may be useful for the identification and understanding of risk factors, prognostic factors, and the comparison treatments. Methods: Statistical lifetimes models were used to highlight the important factors affecting the disease-free times and the total lifetime about a retrospective study conducted at the Hospital das Clinicas, Faculty of Medicine, University of São Paulo, Ribeirão Preto, referring to 54 patients with locally advanced breast cancer with Her-2 overexpression who started neoadjuvant chemotherapy associated with the drug Herceptin® (Trastuzumab) in the time period ranging from years 2008 to 2012. It was used univariate models assuming Weibull distribution with and without the presence of cure fraction under the frequentist and Bayesian approaches. It was also assumed models assuming a dependence structure between the observed times based on the bivariate Block-Basu exponential distribution, on the bivariate Arnold geometric distribution and on the bivariate Basu-Dhar geometric distribution. Results: From the results of the univariate analysis without the presence of covariates, the most appropriate model for the data was the Weibull model in presence of cure rate under a Bayesian approach. By incorporating the covariates in the models, there was best fit of models with cure fraction, which showed that the stage of the disease was a factor affecting disease-free survival and overall survival. From the bivariate analysis results without the presence of covariates, the estimated means for free survival time of the disease assuming the Block- Basu, Arnold and Basu-Dhar models were respectively given by 108, 140 and 111; for the overall survival times the means were given respectively by, 232, 343, 296 months. In presence of covariates, the models showed that the stage of the disease affects the disease-free survivals and the overall survival times. Assuming the Arnold model, the covariate type of surgery also was significant. Conclusions: The results of this study present alternatives for the analysis of survival times in the presence of cure fraction, censoring and covariates. The Cox proportional hazards model not always is apropriate to the database characteristics studied, which requires the search for more suitable statistical models that produce consistent inferences.

SOUZA, R. M. \\Estatstica em Bioequivaência: Garantia na qualidade do medicamento generico\". 2008. 42 f Dissertação (Mestrado em Saude na Comunidade) Faculdade de Medicina de Ribeir~ao Preto - USP Como alternativa aos medicamentos de uso humano de grande circulação no mercado brasileiro foram regulamentados os medicamentos genericos, conforme a Lei dos genericos no 9787/99, que evidenciaram os estudos de bioequivalência e biodisponibilidade no Brasil com o objetivo de avaliar a bioequivalência das formulações genericas, tomando-se como referências os medicamentos ja existentes no mercado e com eficacia comprovada. Duas formulações de um mesmo medicamento são consideradas bioequivalentes se suas biodisponibilidades não apresentam evidências de diferenças signicativas segundo limites clinicamente especificados, denominados limites de bioequivalência. Os estudos de bioequivalência são realizados mediante a administração de duas formulações, sendo que uma esta em teste e a outra e a referência, em um numero de voluntários previamente denidos, usando-se um planejamento experimental, na maioria das vêzes do tipo crossover. Apos a retirada de sucessivas amostras sanguíneas ou urinárias em tempos pre-determinados, estudam-se alguns parâmetros farmacocinéticos como area sob a curva de concentrac~ao, concentrac~ao maxima do farmaco e tempo em que a concentração ao maxima ocorre. Esta dissertação de mestrado introduz alguns conceitos basicos de bioequivalênncia para, logo em seguida, apresentar analises Bayesianas para medidas de bioequivalência tanto univariada como multivariada assumindo a distribuição ao normal multivariada para os dados e também a distribuição de Student multivariada. Uma aplicação a de exemplicar o que foi introduzido e apresentada e, para o conjunto de dados em estudo têm, por meio de criterios de seleção ao de modelos, evidências favoraveis a escolha dos modelos multivariados para a condução deste estudo de bioequivalência media. / SOUZA, R. M. \\Statistics on Bioequivalence: Guarantee in quality of generic drug\". 2008. 42 s Dissertation (Master Degree) Faculdade de Medicina de Ribeir~ao Preto - USP As an alternative to medicines for human use of great movement in Brazil, the use of generic medicines were regulated, according to the law of the generic no 9787/99, which establish the studies of bioavailability and bioequivalence in Brazil in order to evaluate bioequivalence of generic formulations, considering as reference existing medicinal products, with proved ecacy. Two formulations of the same drug are considered bioequivalents if your bioavailability do not present evidence of signicant dierences according to clinically specied limits known as bioequivalence limits. Bioequivalence studies are carried out by the administration of two formulations (one is in test and the other one is the reference) in a pre-dened number of volunteers using an experimental plan that is often the crossover one. After the withdrawn of successive blood or urinary samples in predetermined intervals, some pharmacokinetic parameters were studied, such as area under concentration curve, maximum concentration of drug and time that the maximum concentration occurs. This dissertation introduces some basic concepts of bioequivalence and following that, it is presented Bayesian analysis for both as univariate and as multivariate bioequivalence measures assuming the multivariate normal distribution for the data and also the distribution of multivariate t student distribution. An application in order to illustrate what was introduced is presented in this work, and by using means of selection criteria of models, it was observed that for all data on study, there were evidences that lead to choose the multivariates models in order to conduct this study of average bioequivalence.