A literatura sobre economia monetária vem despertando interesse crescente dentro da macroeconomia. Devido aos avanços computacionais, os modelos têm se tornado cada vez mais complexos e precisos, permitindo estudar detalhadamente as relações entre as variáveis reais da economia e as variáveis nominais. Dessa forma, através de um modelo de equilíbriogeral estocástico e dinâmico (DSGE) baseado em Gali e Monacelli (2005), é proposto e estimado um modelo para a economia brasileira através de métodos bayesianos, com o intuito de avaliar se o Banco Central do Brasil (BCB) considera variações cambiais na condução da política monetária. O resultado mais importante do presente trabalho é que não há evidências de que o BCB altere diretamente a trajetória dos juros devido a variações na taxa de câmbio. Um exercício de simulação também é realizado. Conclui-se que a economia acomoda rapidamente choques induzidos separadamente na taxa de câmbio, nos termos de troca, na taxa de juros e na inflação mundial. / The literature on monetary economy has aroused growing interest in macroeconomics. Due to computational advancements, models have been increasingly more complex and accurate, allowing for the in-depth analysis of the relationships between real economic variables and nominal variables. Therefore, using a dynamic stochastic general equilibrium (DSGE) model, based on Gali and Monacelli (2005), we propose and estimate a model for the Brazilian economy by employing Bayesian methods so as to assess whether the Central Bank of Brazil takes exchange rate fluctuations into account in the conduct of monetary policy. The most striking result of the present study is that the Central Bank of Brazil does not directly change the interest rate path due to exchange rate movements. A simulation exercise is also used. Our conclusion is that the economy quickly accommodates shocks induced separately on the exchange rate, on the terms of trade, on the interest rate, and on global inflation.

Neste trabalho utilizamos um modelo espaço-temporal proposto em Rojas (2004) para medir a interação social da criminalidade na região metropolitana de São Paulo. Realizamos simulações de Monte Carlo para testar a capacidade de estimação do modelo em diferentes cenários. Observamos que a estimação melhora com o aumento de observações ao longo do tempo. Já os resultados empíricos indicam que a região metropolitana de São Paulo é um hot spot no estado, pois é encontrado um maior grau de interação social no índice de homicídio em relação aos índices de roubo e furto. / In this paper we employ a spatio-temporal model proposed in Rojas (2004) to evaluate the social interaction in crime in São Paulo metropolitan area. We carry out Monte Carlo simulations to test the model estimation capability in different scenarios. We notice that the estimation gets better as the number of observations in time raises. The results point out that São Paulo metropolitan area is a hot spot in the state since we found out a greater social interaction for the homicide index, compared to robbery and thievery.

A literatura sobre economia monetária vem despertando interesse crescente dentro da macroeconomia. Devido aos avanços computacionais, os modelos têm se tornado cada vez mais complexos e precisos, permitindo estudar detalhadamente as relações entre as variáveis reais da economia e as variáveis nominais. Dessa forma, através de um modelo de equilíbriogeral estocástico e dinâmico (DSGE) baseado em Gali e Monacelli (2005), é proposto e estimado um modelo para a economia brasileira através de métodos bayesianos, com o intuito de avaliar se o Banco Central do Brasil (BCB) considera variações cambiais na condução da política monetária. O resultado mais importante do presente trabalho é que não há evidências de que o BCB altere diretamente a trajetória dos juros devido a variações na taxa de câmbio. Um exercício de simulação também é realizado. Conclui-se que a economia acomoda rapidamente choques induzidos separadamente na taxa de câmbio, nos termos de troca, na taxa de juros e na inflação mundial. / The literature on monetary economy has aroused growing interest in macroeconomics. Due to computational advancements, models have been increasingly more complex and accurate, allowing for the in-depth analysis of the relationships between real economic variables and nominal variables. Therefore, using a dynamic stochastic general equilibrium (DSGE) model, based on Gali and Monacelli (2005), we propose and estimate a model for the Brazilian economy by employing Bayesian methods so as to assess whether the Central Bank of Brazil takes exchange rate fluctuations into account in the conduct of monetary policy. The most striking result of the present study is that the Central Bank of Brazil does not directly change the interest rate path due to exchange rate movements. A simulation exercise is also used. Our conclusion is that the economy quickly accommodates shocks induced separately on the exchange rate, on the terms of trade, on the interest rate, and on global inflation.

Neste trabalho utilizamos um modelo espaço-temporal proposto em Rojas (2004) para medir a interação social da criminalidade na região metropolitana de São Paulo. Realizamos simulações de Monte Carlo para testar a capacidade de estimação do modelo em diferentes cenários. Observamos que a estimação melhora com o aumento de observações ao longo do tempo. Já os resultados empíricos indicam que a região metropolitana de São Paulo é um hot spot no estado, pois é encontrado um maior grau de interação social no índice de homicídio em relação aos índices de roubo e furto. / In this paper we employ a spatio-temporal model proposed in Rojas (2004) to evaluate the social interaction in crime in São Paulo metropolitan area. We carry out Monte Carlo simulations to test the model estimation capability in different scenarios. We notice that the estimation gets better as the number of observations in time raises. The results point out that São Paulo metropolitan area is a hot spot in the state since we found out a greater social interaction for the homicide index, compared to robbery and thievery.

Neste trabalho será feito um modelo espaço-temporal contínuo para preços de imóveis na cidade de São Paulo estimado através de métodos Bayesianos. Faremos uma decomposição da série em tendência e ciclo além de incorporar um conjunto de variáveis explicativas e efeitos aleatórios espaciais projetados no contínuo. Este modelo introduz um novo método para analisar a formação dos preços dos lançamentos imobiliários. Consideramos em nosso modelo hedônico, além das características intrínsecas, também as características da vizinhança e o ambiente econômico. Com este modelo, conseguimos observar os preços de equilíbrio para as respectivas localizações e uma interpretação mais clara da dinâmica de preços dos imóveis entre janeiro de 2000 e dezembro de 2013 para a cidade de São Paulo. / In this work will be made a continuous spatial-temporal model for real estate prices in the city of São Paulo estimated using Bayesian methods. We will decompose the series into a trend and cycle, and incorporate a set of explanatory variables and random spatial effects projected into the continuum. This model introduces a new method to analyze the price formation of real estate launches. We consider in our hedonic model, besides the intrinsic characteristics, also the characteristics of the neighborhood and the economic environment. With this model, we were able to observe the equilibrium prices for the respective locations and a clearer interpretation of the dynamics of real estate prices between January 2000 and December 2013 for the city of São Paulo.

Neste trabalho utilizamos um modelo espaço-temporal proposto em Rojas (2004) para medir a interação social da criminalidade na região metropolitana de São Paulo. Realizamos simulações de Monte Carlo para testar a capacidade de estimação do modelo em diferentes cenários. Observamos que a estimação melhora com o aumento de observações ao longo do tempo. Já os resultados empíricos indicam que a região metropolitana de São Paulo é um hot spot no estado, pois é encontrado um maior grau de interação social no índice de homicídio em relação aos índices de roubo e furto. / In this paper we employ a spatio-temporal model proposed in Rojas (2004) to evaluate the social interaction in crime in São Paulo metropolitan area. We carry out Monte Carlo simulations to test the model estimation capability in different scenarios. We notice that the estimation gets better as the number of observations in time raises. The results point out that São Paulo metropolitan area is a hot spot in the state since we found out a greater social interaction for the homicide index, compared to robbery and thievery.

Neste trabalho apresentamos os mais importantes processos de estimação em algumas classes de modelos de resposta ao item (Dicotômicos e Policotômicos). Discutimos algumas propriedades desses métodos. Com o objetivo de comparar o desempenho dos métodos conduzimos simulações apropriadas. / In this work we show the most important estimation methods for some item response models (both dichotomous and polichotomous). We discuss some proprieties of these methods. To compare the characteristic of these methods we conducted appropriate simulations.

Des modèles de plus en plus précis sont développés pour prédire le comportement vibroacoustique des structures et dimensionner des traitements adaptés. Or, les sources vibratoires, qui servent de données d'entrée à ces modèles, restent assez souvent mal connues. Une erreur sur les sources injectées se traduit donc par un biais sur la prédiction vibroacoustique. En amont des simulations, la caractérisation expérimentale de sources vibratoires en conditions opérationnelles est un moyen de réduire ce biais et fait l'objet de ces travaux de thèse.L'approche proposée utilise une méthode inverse, la Résolution Inverse (RI), permettant l'identification de sources à partir des déplacements de structure. La sensibilité aux perturbations de mesure, commune à la plupart des méthodes inverses, est traitée dans un cadre probabiliste par des méthodes bayésiennes.Ce formalisme bayésien permet : d'améliorer la robustesse de la méthode RI ; la détection automatique de sources sur la distribution spatiale ; l'identification parcimonieuse pour le cas de sources ponctuelles ; l'identification de paramètres de modèle pour les structures homogénéisées ; l'identification de sources instationnaires ; la propagation des incertitudes de mesures sur l'évaluation du spectre d'effort ; l'évaluation de la qualité de la mesure par un indicateur empirique de rapport signal à bruit.Ces deux derniers points sont obtenus avec une unique mesure, là où des approches statistiques plus classiques demandent une campagne de mesures plus conséquente. Ces résultats ont été validés à la fois numériquement et expérimentalement, avec une source maîtrisée mais aussi avec une source industrielle. De plus, la procédure est en grande partie non-supervisée. Il ne reste alors à la charge de l’utilisateur qu’un nombre restreint de paramètres à fixer. Lesapproches proposées peuvent donc être utilisées dans une certaine mesure comme des boites noires. / Increasingly accurate models are developped to predict the vibroacoustic behavior of structures and to propose adequate treatments.Vibration sources used as input of these models are still broadly unknown. In simulation, an error on vibration sources produces a bias on the vibroacoustic predictions. A way to reduce this bias is to characterize experimentally the vibration sources in operational condition before some simulations. It is therefore the subject of this PhD work.The proposed approach is based on an inverse method, the Force Analysis Technique (FAT), and allows the identification of vibration sources from displacement measurements. The noise sensibility, common to most of inverse methods, is processed in a probabilistic framework using Bayesian methods.This Bayesian framework allows: some improvements of the FAT robustness; an automatic detection of sources; the sparse identification of sources for pointwise sources; the model parameters identification for the purpose of homogenized structures; the identification of unsteady sources; the propagation of uncertainties through force spectrum (with credibility intervals); measurement quality assessment from a empirical signal to noise ratio.These two last points are obtained from a unique scan of the structure, where more traditional statistical methods need multiple scans of the structure. Both numerical and experimental validations have been proposed, with a controled excitation and with an industrial source. Moreover, the procedure is rather unsupervised in this work. Therefore, the user only has a few number of parameters to set by himself. In a certain extent, the proposed approaches can then be applied as black boxes.

Un algorithme de décomposition des unités motrices constituant un signal électromyographiques intramusculaires (iEMG) a été proposé au laboratoire LS2N. Il s'agit d'un filtrage bayésien estimant l'état d'un modèle de Markov caché. Cet algorithme demande beaucoup de temps d'execution, même pour un signal ne contenant que 4 unités motrices. Dans notre travail, nous avons d'abord validé cet algorithme dans une structure série. Nous avons proposé quelques modifications pour le modèle de recrutement des unités motrices et implémenté deux techniques de pré-traitement pour améliorer la performance de l'algorithme. Le banc de filtres de Kalman a été remplacé par un banc de filtre LMS. Le filtre global consiste en l'examen de divers scénarios arborescents d'activation des unités motrices: on a introduit deux techniques heuristiques pour élaguer les divers scénarios. On a réalisé l'implémentation GPU de cet algorithme à structure parallèle intrinsèque. On a réussi la décomposition de 10 signaux expérimentaux enregistrés sur deux muscules, respectivement avec électrode aiguille et électrode filaire. Le nombre d'unités motrices est de 2 à 8. Le pourcentage de superposition des potentiels d'unité motrice, qui représente la complexité de signal, varie de 6.56 % à 28.84 %. La précision de décomposition de tous les signaux sont plus que 90 %, sauf deux signaux en 30 % MVC , sauf pour deux signaux qui sont à 30 % MVC et dont la précision de décomposition est supérieure à 85%. Nous sommes les premiers à réaliser la décomposition en temps réel pour un signal constitué de 10 unités motrices. / :A sequential decomposition algorithm based on a Hidden Markov Model of the EMG, that used Bayesian filtering to estimate the unknown parameters of discharge series of motor units was previously proposed in the laboratory LS2N. This algorithm has successfully decomposed the experimental iEMG signal with four motor units. However, the proposed algorithm demands a high time consuming. In this work, we firstly validated the proposed algorithm in a serial structure. We proposed some modifications for the activation process of the recruitment model in Hidden Markov Model and implemented two signal pre-processing techniques to improve the performance of the algorithm. Then, we realized a GPU-oriented implementation of this algorithm, as well as the modifications applied to the original model in order to achieve a real-time performance. We have achieved the decomposition of 10 experimental iEMG signals acquired from two different muscles, respectively by fine wire electrodes and needle electrodes. The number of motor units ranges from 2 to 8. The percentage of superposition, representing the complexity of iEMG signal, ranges from 6.56 % to 28.84 %. The accuracies of almost all experimental iEMG signals are more than90 %, except two signals at 30 % MVC (more than 85 %). Moreover, we realized the realtime decomposition for all these experimental signals by the parallel implementation. We are the first one that realizes the real time full decomposition of single channel iEMG signal with number of MUs up to 10, where full decomposition means resolving the superposition problem. For the signals with more than 10 MUs, we can also decompose them quickly, but not reaching the real time level.

This thesis is focused on the study of advanced methods of interest rate mo- dels calibration. The theoretical part provides introduction to basic terminology of financial mathematics, financial, concretely interest rate derivatives. It presents interest rate models, it is mainly aimed at HJM approach and describes in detail the Libor market model, then introduces the use of Bayesian principle in calcula- ting the probability of MCMC methods. At the end of this section the methods of calibration of volatility to market data are described. The last chapter consists of the practical application of different methods of calibration Libor market model and consequently pricing od interest rate swaption. The introduction describes procedure of arrangement of input data and process of pricing of interest rate derivatives. It is consequently used for the valuation of derivative contract accor- ding to mentioned methods. 1