One of the main classes of spatial epidemiological studies is disease mapping, where the main aim is to describe the overall disease distribution on a map, for example, to highlight areas of elevated or lowered mortality or morbidity risk, or to identify important social or environmental risk factors adjusting for the spatial distribution of the disease. This thesis focused and proposed solutions to two most commonly seen obstacles in disease mapping applications, the changing census boundaries due to long study period and data aggregation for patients' confidentiality. In disease mapping, when target diseases have low prevalence, the study usually covers a long time period to accumulate sufficient cases. However, during this period, numerous irregular changes in the census regions on which population is reported may occur, which complicates inferences. A new model was developed for the case when the exact location of the cases is available, consisting of a continuous random spatial surface and fixed effects for time and ages of individuals. The process is modelled on a fine grid, approximating the underlying continuous risk surface with Gaussian Markov Random Field and Bayesian inference is performed using integrated nested Laplace approximations. The model was applied to clinical data on the location of residence at the time of diagnosis of new Lupus cases in Toronto, Canada, for the 40 years to 2007, with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are produced for Lupus and, for comparison, Psoriatic Arthritis data from the same clinic. Simulation studies are also carried out to better understand the performance of the proposed model as well as to compare with existing methods. When the exact locations of the cases are not known, inference is complicated by the uncertainty of case locations due to data aggregation on census regions for confidentiality. Conventional modelling relies on the census boundaries that are unrelated to the biological process being modelled, and may result in stronger spatial dependence in less populated regions which dominate the map. A new model was developed consisting of a continuous random spatial surface with aggregated responses and fixed covariate effects on census region levels. The continuous spatial surface was approximated by Markov random field, greatly reduces the computational complexity. The process was modelled on a lattice of fine grid cells and Bayesian inference was performed using Markov Chain Monte Carlo with data augmentation. Simulation studies were carried out to assess performance of the proposed model and to compare with the conventional Besag-York-Molli\'e model as well as model assuming exact locations are known. Receiver operating characteristic curves and Mean Integrated Squared Errors were used as measures of performance. For the application, surveillance data on the locations of residence at the time of diagnosis of syphilis cases in North Carolina, for the 9 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are also produced, identifying Lumberton as a ``syphilis hotspot".

Topological maps are light-weight, graphical representations of environments that are scalable and amenable to symbolic manipulation. Thus, they are well- suited for basic robot navigation applications, and also provide a representational basis for the procedural and semantic information needed for higher-level robotic tasks. However, their widespread use has been impeded in part by the lack of reliable, general purpose algorithms for their construction. In this dissertation, I present a probabilistic framework for the construction of topological maps that addresses topological ambiguity, is failure-aware, computa- tionally efficient, and can incorporate information from various sensing modalities. The framework addresses the two major problems of topological mapping, namely topological ambiguity and landmark detection. The underlying idea behind overcoming topological ambiguity is that the com- putation of the Bayesian posterior distribution over the space of topologies is an effective means of quantifying this ambiguity, caused due to perceptual aliasing and environment variability. Since the space of topologies is combinatorial, the posterior on it cannot be computed exactly. Instead, I introduce the concept of Probabilistic Topological Maps (PTMs), a sample-based representation that ap- proximates the posterior distribution over topologies given the available sensor measurements. Sampling algorithms for the efficient computation of PTMs are described. The PTM framework can be used with a wide variety of landmark detection schemes under mild assumptions. As part of the evaluation, I describe a novel landmark detection technique that makes use of the notion of "surprise" in mea- surements that the robot obtains, the underlying assumption being that landmarks are places in the environment that generate surprising measurements. The com- putation of surprise in a Bayesian framework is described and applied to various sensing modalities for the computation of PTMs. The PTM framework is the first instance of a probabilistic technique for topo- logical mapping that is systematic and comprehensive. It is especially relevant for future robotic applications which will need a sparse representation capable of accomodating higher level semantic knowledge. Results from experiments in real environments demonstrate that the framework can accomodate diverse sensors such as camera rigs and laser scanners in addition to odometry. Finally, results are pre- sented using various landmark detection schemes besides the surprise-based one.

Made available in DSpace on 2015-03-26T13:32:13Z (GMT). No. of bitstreams: 1 texto completo.pdf: 3841900 bytes, checksum: 0cacdbb43213a6d667c6941e828f2c74 (MD5) Previous issue date: 2012-02-17 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A basic issue in animal genetic improvement is if the selection of animals practiced in a given environment results in genetic progress in other environment. The presence of genotype environment interaction (GEI) is characterized by different response of genotypes to environmental variations, which can cause change in the classification of the genotypes in different environments. Among the ways to evaluate the GEI, models of reaction norms (MRN) have been distinguished themselves worldwide today. The GEI is a linear covariance function that lets you assign to each animal, two random regression coefficients (intercept and slope) that predict the genetic value depending on the environment. Thus, each animal has a genetic value for each environment. This study aims to verify the presence of GEI for weaning weight in Nelore Mocho created in different regions of Brazilian northeast, using the model of reaction norms. It was adjusted two models of norms of reaction to the data, MRN in two steps and MRN in one step. The first uses a model without considering the genotype environment interaction to obtain estimates of the environment effects and then uses them as a known covariate in a random regression model. The second, under the Bayesian approach estimates all parameters jointly. The analyzes were conducted using software SAS, R, AMC and Intergen. Based on two of the three criteria used for choosing the model the was the MRN in one step. Through this model it was possible to verify the presence of genotype environment interaction and to estimate the genetic value of animals for weaning weight in each producing region in the Northeast. Thus, it is possible to recommend the most appropriate sires for each environment studied, taking advantage of the GEI effects. / Uma questão básica no melhoramento genético animal é se a seleção dos animais praticada em um determinado ambiente resulta em progresso genético em outro tipo de ambiente. A presença de interação genótipos ambientes (IGA) é caracterizada pela resposta diferenciada dos genótipos às variações ambientais, o que pode ocasionar alteração na classificação dos genótipos nos diferentes ambientes. Dentre as formas de se avaliar a IGA, os modelos de norma de reação (MNR) têm se destacado, atualmente, em todo o mundo. O MNR linear é uma função de covariância que permite atribuir a cada animal, dois coeficientes de regressão aleatórios (intercepto e inclinação) que predizem o valor genético em função do ambiente. Assim, cada animal terá um valor genético predito para cada ambiente. Este estudo tem o objetivo de verificar a presença de IGA para peso à desmama em bovinos da raça Nelore Mocho criados em diferentes regiões produtoras no Nordeste do Brasil, utilizando o modelo de normas de reação. Ajustou-se dois modelos de normas de reação aos dados, MNR em dois passos e o MNR em um passo. O primeiro utiliza um modelo sem considerar a interação genótipos ambientes para obter estimativas dos efeitos de ambiente e em seguida as utiliza como uma covariável conhecida em um modelo de regressão aleatória e o segundo, sob o enfoque Bayesiano, estima todos os parâmetros simultaneamente. As análises foram realizadas por meio dos softwares SAS, R, AMC e Intergen. Com base em dois dos três critérios utilizados para escolha do modelo, o que melhor se ajustou aos dados foi o MNR em um passo. Por meio deste modelo foi possível verificar a presença de interação genótipos ambientes e estimar o valor genético dos animais para cada região produtora do Nordeste, para a característica peso à desmama. Assim, é possível recomendar os reprodutores mais apropriados para cada ambiente estudado, capitalizando os efeitos da IGA.

Mathematical modeling has been instrumental to the development of natural sciences over the last half-century. Through iterated interactions between modeling and real-world exper- imentation, these models have furthered our understanding of the processes in biology and chemistry that they seek to represent. In certain application domains, such as the field of car- diac biology, communities of modelers with common interests have emerged, leading to the development of many models that attempt to explain the same or similar phenomena. As these communities have developed, however, reporting standards for modeling studies have been in- consistent, often focusing on the final parameterized result, and obscuring the assumptions and data used during their creation. These practices make it difficult for researchers to adapt exist- ing models to new systems or newly available data, and also to assess the identifiability of said models - the degree to which their optimal parameters are constrained by data - which is a key step in building trust that their formulation captures truth about the system of study. In this thesis, we develop tools that allow modelers working with biological or chemical time series data to assess identifiability in an automated fashion, and embed these tools within a novel online community resource that enforces reproducible standards of reporting and facilitates exchange of models and data. We begin by exploring the application of Bayesian and approximate Bayesian inference methods, which parameterize models while simultaneously assessing uncertainty about these estimates, to assess the identifiability of models of the cardiac action potential. We then demon- strate how the side-by-side application of these Bayesian and approximate Bayesian methods can be used to assess the information content of experiments where system observability is limited to "summary statistics" - low-dimensional representations of full time-series data. We next investigate how a posteriori methods of identifiability assessment, such as the above inference techniques, compare against a priori methods based on model structure. We compare these two approaches over a range of biologically relevant experimental conditions, and high- light the cases under which each strategy is preferable. We also explore the concept of optimal experimental design, in which measurements are chosen in order to maximize model identifia- bility, and compare the feasibility of established a priori approaches against a novel a posteriori approach. Finally, we propose a framework for representing and executing modeling experiments in a reproducible manner, and use this as the foundation for a prototype "Modeling Web Lab" where researchers may upload specifications for and share the results of the types of inference explored in this thesis. We demonstrate the Modeling Web Lab's utility across multiple mod- eling domains by re-creating the results of a contemporary modeling study of the hERG ion channel model, as well as the results of an original study of electrochemical redox reactions. We hope that this works serves to highlight the importance of both reproducible standards of model reporting, as well as identifiability assessment, which are inherently linked by the desire to foster trust in community-developed models in disciplines across the natural sciences.

[pt] O objetivo deste trabalho é desenvolver uma estratégia Metropolis-Hastings para inferência Bayesiana, usando a estrutura ARCH multivatriada com representação BEKK.Em problemas complexos, como a generalização ARCH/GARCH univariadas para estruturas multivariadas, o processo de inferência é dificultado por causa do número de parâmetros envolvidos e das restrições a que eles estão sujeitos. Neste trabalho desenvolvemos uma estratégia Metropolis- Hastings para inferência Bayesiana, usando uma estrutura ARCH multivariada com representação BEKK. / [en] The objective of this work is to develop Metropolis-Hasting for strategy Bayesian Inference, based on a Multivariate ARCH model with BEKK representation. In complex problems, such as the multivariate generalization of ARCH/GARCH structures, the inference process in complicated, due to the large number of parameters involved and to the restrictions they must satisfy. We propose Metropolis- Hastings structure to provide inference, in a Bayesian framework, for a multivariate ARCH model with BEKK representation. / [es] EL objetivo de este trabajo es desarrollar una estrategia Metropolis-Hastings para inferencia Bayesiana, usando La extructura ARCH multivatriada con representación BEKK.En problemas complejos, como la generalización ARCH/GARCH univariadas para extructuras multivariadas, el proceso de inferencia se hace dificil por causa del número de parámetros involucrados y de las restricciones a que ellos están sujetos. En este trabajo desarrollamos una estrategia Metropolis- Hastings para inferencia Bayesiana, usando una extructura ARCH multivariada con representación BEKK.

We propose a statistical model for weighted temporal networks capable of measuring the level of heterogeneity in a financial system. Our model focuses on the level of diversification of financial institutions; that is, whether they are more inclined to distribute their assets equally among partners, or if they rather concentrate their commitment towards a limited number of institutions. Crucially, a Markov property is introduced to capture time dependencies and to make our measures comparable across time. We apply the model on an original dataset of Austrian interbank exposures. The temporal span encompasses the onset and development of the financial crisis in 2008 as well as the beginnings of European sovereign debt crisis in 2011. Our analysis highlights an overall increasing trend for network homogeneity, whereby core banks have a tendency to distribute their market exposures more equally across their partners.

[pt] Os métodos clássicos para estudo de valores extremos de séries temporais se apóiam nas chamadas distribuições de extremos. Uma alternativa é o método P.O.T. (Peaks Over Threshold), desenvolvido por hidrologistas, o qual estuda apenas os valores da série que excedem um dado patamar. Esses procedimentos são baseados em hipóteses restritivas. Nesse trabalho desenvolvemos modelos sobre extremos que podem ser utilizados em situações mais gerais. Eles são essencialmente modelos lineares dinâmicos com inferência Bayesiana, nos quais as observações têm um distribuição de extremos. Embora essas distribuições não sejam da famí­lia exponencial, toda a análise é feita explicitamente, sem aproximações numéricas. Tratamos ainda da construção de distribuições a priori não informáticas. Finalmente, a partir desses modelos retomamos problemas clássicos de previsão de extremos. / [en] The classical approaches for extreme values studies make use the so called Extreme Values Distribution. An alternative approach, known as P.O.T. (Peaks Over Threshold) developed by hydrologists considers only excedances over a given threshold value. All the existing approaches are in a sense, based on constrained hupothesis. In this thesis we developed forecasting models for extreme values that are dynamic linear model as the underlying formulation, and the Bayesian inference. Although the process observation follows an extreme values distribution and, therefore not a member of the exponential family, we were able to formulate explicitly the model with no use of numerical approximations throughout, Concerning the parameter priors, we use in the model formulation the Jeffery`s non informative prior.

Cette thèse se compose de trois chapitres qui présentent trois articles indépendants portant sur l’application de modèles dynamiques dans les domaines du marketing et de la finance. Le premier article adopte une approche structurelle des modèles dynamiques pour analyser la relation entre les revenus et l’impact des taxes sur les produits de grignotage (fat taxes). Le deuxième et le troisième article utilisent des modèles dynamiques en forme réduite : nous y mobilisons des modèles hiérarchiques dynamiques qui intègrent un cadre bayésien hiérarchique à un Modèle Linéaire Dynamique. Le second article étudie la tarification dynamique des produits de saison à l’aide d’un modèle hiérarchique dynamique et flexible. Le troisième article analyse le coût du financement des opérations commerciales au cours de la crise financière de 2008-2009 au moyen d’un modèle hiérarchique dynamique. Dans le premier article, nous utilisons un modèle structurel dynamique pour analyser la corrélation entre les revenus et l’impact d’une taxe sur les produits de grignotage (ou fat tax). Les résultats montrent qu’une telle taxe a moins d’impact sur les individus à faibles revenus que sur ceux dont les revenus sont plus élevés, dans la mesure où le premier groupe a davantage tendance à consommer des snacks. Dans le second article, nous élaborons un modèle hiérarchique dynamique et flexible pour estimer la trajectoire des sensibilités-prix afin d’en déduire le tarif dynamique des produits de saison. Nous constatons que les prix optimaux dépendent de la composition de la clientèle du magasin, et que les vendeurs de produits de saisons peuvent en tirer profit lorsqu’ils fixent leurs tarifs. Dans le troisième article, nous utilisons un modèle hiérarchique dynamique pour étudier l’impact de quatre indicateurs macroéconomiques sur le coût du financement des opérations commerciales pendant la crise financière de 2008-2009, ainsi que pendant les périodes qui l’ont précédée et suivie. Nous constatons que l’impact de trois de ces facteurs macroéconomiques (croissance du PIB, échanges commerciaux et inflation) sur le financement commercial est conforme aux prédictions théoriques, tandis que l’impact du quatrième facteur (capitalisation boursière) semble assez surprenant. / This dissertation consists of three chapters that present three standalone essays on the application of dynamic models to marketing and finance. The first essay uses a structural approach to dynamic models to study the role of income on the impact of fat taxes. The second and third essays use a reduced form approach to dynamic models: we use dynamic hierarchical models which incorporate a Hierarchical Bayesian framework in a Dynamic Linear Model. The second essay studies the dynamic pricing of seasonal goods with the help of a flexible dynamic hierarchical model. The third essay studies the cost of trade finance during the financial crisis of 2008-2009 using a dynamic hierarchical model. In the first essay, we use a dynamic structural model to investigate how income interacts with the impact of a “fat tax” (a tax on snack food). We find that the low-income group is less impacted by a “fat tax” compared to the higher income group as they have a higher tendency to consume snack food. In the second essay, we develop a flexible dynamic hierarchical model to estimate the trajectory of price sensitivities which allows us to infer the dynamic prices of seasonal goods. We find that optimal prices depend on the customer composition of the store, and seasonal goods retailers can take advantage of this while setting prices. In the third essay, using a dynamic hierarchical model we examine the impact of four macroeconomic indicators on trade finance costs in and around the financial crisis of 2008-2009. We find the impact of three of these macroeconomic factors (GDP growth, trade and inflation) on trade finance to be in line with the theory, while the impact of the fourth factor (stock market capitalization) on trade finance appears somewhat surprising.

O monitoramento automático de pacientes é um recurso essencial em hospitais para o bom gerenciamento de cuidados médicos. Enquanto que alarmes devido a condições fisiológicas anormais são importantes para o rápido tratamento, estes também podem ser uma fonte de ruídos desnecessários devido a falsos alarmes causados por interferência eletromagnética ou movimentação de sensores. Uma fonte significativa de falsos alarmes é relacionada com a frequência cardíaca, o qual é disparado quando o ritmo cardíaco do paciente está muito rápido ou muito lento. Neste trabalho, a fusão de diferentes sensores fisiológicos é explorada para fazer uma estimativa robusta de frequência cardíaca. Um conjunto de algoritmos utilizando índice de variabilidade cardíaca, inferência bayesiana, redes neurais, lógica fuzzy e votador majoritário são propostos para fundir a informação do eletrocardiograma, pressão sanguínea e fotopletismograma. Três informações básicas são extraídas de cada sensor: variabilidade cardíaca, a diferença de frequência cardíaca entre os sensores e a análise espectral. Estas informações são usadas como entradas para os algoritmos. Quarenta gravações selecionadas do banco de dados MIMIC são usadas para validar o sistema. Finalmente, a frequência cardíaca calculada é comparada com as anotações do banco de dados. Resultados mostram que a fusão utilizando redes neurais apresenta a melhor redução de falsos alarmes de 89.33%, enquanto que a técnica bayesiana apresenta uma redução de 83.76%. A lógica fuzzy mostrou uma redução de 77.96%, o votador majoritário 61.25% e o índice de variabilidade cardíaca de 65.43%. Portanto, os algoritmos propostos mostraram bom desempenho e podem ser muito úteis em monitores de sinais vitais modernos. / Automatic patient monitoring is an essential resource in hospitals for good health care management. While alarms due to abnormal physiological conditions are important to deliver fast treatment, it can be also a source of unnecessary noise due to false alarms caused by electromagnetic interference or motion artifacts. One significant source of false alarms are those related to heart rate, which is triggered when the heart rhythm of the patient is too fast or too slow. In this work, the fusion of different physiological sensors is explored in order to create a robust heart rate estimation. A set of algorithms using heart rate variability index, bayesian inference, neural networks, fuzzy logic and majority voting is proposed to fuse information from electrocardiogram, arterial blood pressure and photoplethysmogram. Three basic informations are extracted from each source, namely, heart rate variability, the heart rate difference between sensors and the spectral analysis. These informations are used as inputs to the algorithms. Forty selected recordings from MIMIC database was used to validate the system. Finally, the calculated heart rate is compared with the database annotation. Results show that neural networks fusion presents the best false alarms reduction of 89.33%, while the bayesian technique presents an error reduction of 83.76%. Fuzzy logic showed an error reduction of 77.96%, majority voting 61.25% and the heart rate variability index 65.43%. Therefore, the proposed algorithms showed good performance and can be very useful for modern bedside monitors.

Conhecer a distribuição geográfica das espécies é primordial para a tomada de ações efetivas de conservação. Modelos de ocupação são ferramentas importantes para estimar a distribuição das espécies, especialmente quando as informações são incompletas, como é o caso de muitas espécies ameaçadas ou em áreas ainda insuficientemente amostradas. O objetivo deste estudo é ampliar e refinar o conhecimento sobre a distribuição geográfica da toninha, Pontoporia blainvillei, um pequeno cetáceo ameaçado de extinção restrito às águas costeiras do Atlântico Sul ocidental, através de modelos de ocupação. Foram realizadas amostragens aéreas com 4 observadores independentes, em 2058 sítios de 4x4km na distribuição da espécie no Brasil. Foram utilizadas cinco covariáveis de detecção (transparência da água, escala Beaufort, reflexo solar, posição dos amostradores e número de amostradores) e três covariáveis de ocupação (batimetria, temperatura média e produtividade primária) com índices de correlação de Pearson menor que 0,7. Todas as covariáveis contínuas foram estandardizadas com média zero e desvio padrão igual a um. Os modelos de ocupação com autocorrealação espacial foram estimados com Inferência Bayesiana utilizando priors ‘vagos’ (média zero e variância 1.0E6). Em apenas 75 sítios foram detectadas toninhas. A probabilidade de detecção média foi de 0.23 (CRI 0.006 a 0.51), onde as covariáveis Beaufort (efeito negativo), reflexo solar (efeito negativo) e transparência da água (efeito positivo) apresentaram efeitos significativos. A média estimada de ocupação foi de 0,066 (CRI 0,01 a 0,31). As covariáveis batimetria e a temperatura média apresentaram efeitos positivos e negativos sobre o processo de ocupação, respectivamente. Espacialmente o modelo prevê três áreas com altas probabilidades de ocupação aparentemente disjuntas: a) costa norte do Rio de Janeiro; b) costas norte de 3 Santa catarina até São Paulo; c) costa do Rio Grande do Sul. Assim, agregamos importantes informações para a conservação da espécie e realização de novos estudos, apontando onde podemos encontrar maiores probabilidade de ocupação na costa do Brasil e covariáveis que determinam a ocupação e a detecção da espécie. / Knowing the geographic distribution of a species is essential for taking effective conservation actions. Occupation Models are important tools for estimating species distribution, especially when information is incomplete, as is the case with many endangered species or in under-sampled areas. The aim of this study is to expand and refine the knowledge about the geographic distribution of the franciscana, Pontoporia blainvillei, a threatened small cetacean restricted to the coastal waters of the western South Atlantic, through Occupation Models. Aerial samplings were carried out with 4 independent observers, in 2058 sites of 4x4km across the distribution of the species in Brazilian waters. Five detection covariates were used (water transparency, Beaufort scale, solar reflectance, observer position and number of observers) and three covariates of occupation (bathymetry, mean temperature and primary productivity) with Pearson correlation indices less than 0.7. All continuous covariates were standardized with mean zero and standard deviation equal to one. Occupancy Models with spatial autocorrection were estimated using Bayesian Inference using 'vague' priors (zero mean and variance 1.0E6). Franciscana was detected only in 75 sites. The average detection probability 4 was 0.23 (CRI 0.006 to 0.51), where Beaufort (negative effect), solar reflex (negative effect) and water transparency (positive effect) covariables had significant effects. The estimated mean occupancy was 0.066 (CRI 0.01 to 0.31). The bathymetry and the mean temperature covariables had positive and negative effects on the occupation process, respectively. Spatially the model predicts three apparently disjunct areas with high probability of occupation: a) north coast of Rio de Janeiro; b) north coasts of Santa Catarina to São Paulo; c) coast of Rio Grande do Sul. Thus, we add important information for the conservation of species and new studies, pointing out where we can find greater likelihood of occupation on the coast of Brazil and covariates that determine the occupation and the detection of the species.