Recentemente, Guiol, Machado e Schinazi propuseram um modelo estocástico para a evolução de espécies. Nesse modelo, as intensidades de nascimentos de novas espécies e de ocorrências de extinções são invariantes ao longo do tempo. Ademais, no instante de nascimento de uma nova espécie, a mesma é rotulada com um número aleatório gerado de uma distribuição absolutamente contínua. Toda vez que ocorre uma extinção, apenas uma espécie morre - a com o menor número vinculado. Quando a intensidade com que surgem novas espécies é maior que a com que ocorrem extinções, existe um valor crítico f_c tal que todas as espécies rotuladas com números menores que f_c morrerão quase certamente depois de um tempo aleatório finito, e as rotuladas com números maiores que f_c terão probabilidades positivas de se tornarem perpétuas. No entanto, espécies menos aptas continuam a aparecer durante o processo evolutivo e não há a garantia do surgimento de uma espécie imortal. Consideramos um caso particular do modelo de Guiol, Machado e Schinazi e abordamos estes dois últimos pontos. Caracterizamos o processo pontual limite vinculado às espécies na fase subcrítica do modelo e discorremos sobre a existência de espécies imortais. / Recently, Guiol, Machado and Schinazi proposed a stochastic model for species evolution. In this model, births and deaths of species occur with intensities invariant over time. Moreover, at the time of birth of a new species, it is labeled with a random number sampled from an absolutely continuous distribution. Each time there is an extinction event, exactly one existing species disappears: that with the smallest number. When the birth rate is greater than the extinction rate, there is a critical value f_c such that all species that come with number less than f_c will almost certainly die after a finite random time, and those with numbers higher than f_c survive forever with positive probability. However, less suitable species continue to appear during the evolutionary process and there is no guarantee the emergence of an immortal species. We consider a particular case of Guiol, Machado and Schinazi model and approach these last two points. We characterize the limit point process linked to species in the subcritical phase of the model and discuss the existence of immortal species.

Neste trabalho, aplicamos o algoritmo Breadth-First Search para encontrar o tamanho de uma componente conectada no grafo aleatório de Erdos-Rényi. Uma cadeia de Markov é obtida deste procedimento. Apresentamos alguns resultados bem conhecidos sobre o comportamento dessa cadeia de Markov. Combinamos alguns destes resultados para obter uma proposição sobre a probabilidade da componente atingir um determinado tamanho e um resultado de convergência do estado da cadeia neste instante. Posteriormente, aplicamos o teorema de convergência de Darling (2002) a sequência de cadeias de Markov reescaladas e indexadas por N, o número de vértices do grafo, para mostrar que as trajetórias dessas cadeias convergem uniformemente em probabilidade para a solução de uma equação diferencial ordinária. Deste resultado segue a bem conhecida lei fraca dos grandes números para a componente gigante do grafo aleatório de Erdos-Rényi, no caso supercrítico. Além disso, obtemos o limite do fluído para um modelo epidêmico que é uma extensão daquele proposto em Kurtz et al. (2008). / In this work, we apply the Breadth-First Search algorithm to find the size of a connected component of the Erdos-Rényi random graph. A Markov chain is obtained of this procedure. We present some well-known results about the behavior of this Markov chain, and combine some of these results to obtain a proposition about the probability that the component reaches a certain size and a convergence result about the state of the chain at that time. Next, we apply the convergence theorem of Darling (2002) to the sequence of rescaled Markov chains indexed by N, the number of vertices of the graph, to show that the trajectories of these chains converge uniformly in probability to the solution of an ordinary dierential equation. From the latter result follows the well-known weak law of large numbers of the giant component of the Erdos-Renyi random graph, in the supercritical case. Moreover, we obtain the uid limit for an epidemic model which is an extension of that proposed in Kurtz et al. (2008).

Dans cette thèse, nous nous intéressons à l'estimation de paramètres dans les chaînes de Markov cachées. Nous considérons tout d'abord le problème de l'estimation en ligne (sans sauvegarde des observations) au sens du maximum de vraisemblance. Nous proposons une nouvelle méthode basée sur l'algorithme Expectation Maximization appelée Block Online Expectation Maximization (BOEM). Cet algorithme est défini pour des chaînes de Markov cachées à espace d'état et espace d'observations généraux. Dans le cas d'espaces d'états généraux, l'algorithme BOEM requiert l'introduction de méthodes de Monte Carlo séquentielles pour approcher des espérances sous des lois de lissage. La convergence de l'algorithme nécessite alors un contrôle de la norme Lp de l'erreur d'approximation Monte Carlo explicite en le nombre d'observations et de particules. Une seconde partie de cette thèse se consacre à l'obtention de tels contrôles pour plusieurs méthodes de Monte Carlo séquentielles. Nous étudions enfin des applications de l'algorithme BOEM à des problèmes de cartographie et de localisation simultanées. La dernière partie de cette thèse est relative à l'estimation non paramétrique dans les chaînes de Markov cachées. Le problème considéré est abordé dans un cadre précis. Nous supposons que (Xk) est une marche aléatoire dont la loi des incréments est connue à un facteur d'échelle a près. Nous supposons que, pour tout k, Yk est une observation de f(Xk) dans un bruit additif gaussien, où f est une fonction que nous cherchons à estimer. Nous établissons l'identifiabilité du modèle statistique et nous proposons une estimation de f et de a à partir de la vraisemblance par paires des observations. / This document is dedicated to inference problems in hidden Markov models. The first part is devoted to an online maximum likelihood estimation procedure which does not store the observations. We propose a new Expectation Maximization based method called the Block Online Expectation Maximization (BOEM) algorithm. This algorithm solves the online estimation problem for general hidden Markov models. In complex situations, it requires the introduction of Sequential Monte Carlo methods to approximate several expectations under the fixed interval smoothing distributions. The convergence of the algorithm is shown under the assumption that the Lp mean error due to the Monte Carlo approximation can be controlled explicitly in the number of observations and in the number of particles. Therefore, a second part of the document establishes such controls for several Sequential Monte Carlo algorithms. This BOEM algorithm is then used to solve the simultaneous localization and mapping problem in different frameworks. Finally, the last part of this thesis is dedicated to nonparametric estimation in hidden Markov models. It is assumed that the Markov chain (Xk) is a random walk lying in a compact set with increment distribution known up to a scaling factor a. At each time step k, Yk is a noisy observations of f(Xk) where f is an unknown function. We establish the identifiability of the statistical model and we propose estimators of f and a based on the pairwise likelihood of the observations.

[pt] Neste trabalho são estudados os moduladores delta desenvolvido a partir do conhecimento estatístico disponível sobre o sinal a ser transmitido. Estes moduladores são aqui chamados estruturados. Após uma rápida introdução à modulação delta, são descritos alguns sistemas mais conhecidos. Os sistemas estruturados são então formalmente caracterizados e uma análise teórica é desenvolvida, sendo apontadas as dificuldades analíticas envolvidas. A partir de uma configuração básica proposta por C.L. Song, são desenvolvidas equações gerais diferentes das por ele obtidas. A particularização destas equações para sinais Gauss Markov de primeira Ordem dá origem ao chamado sistema Song modificado. Resultados obtidos a partir da simulação digital do sistema de song, do sistema de Song modificado, e do sistema delta simples, são apresentados. Um processo adaptativo para aumentar a faixa dinâmica é proposto com base nos resultados de simulação. / [en] This work examines delta modulation systems in which statistical knowledge about the signal to be tranmitted is explicitly used in sistem design. These modulators are called here structured delta modulators. After a brief introduction to delta modulation some well-known systems are described. Structured systems are then formally defined and analytical difficulties in finding general solutions are pointed out. Starting from a system proposed by C.L. Song, general equations are derived. These equations, which are more complete than the ones obtained by Song are then specialized to first-order Gauss-Maarkov signals, leading to what has been called a modified Song modulators. Digital simulations results are then obtained for song modulators, modified Song modulators and linear delta modulators. An adptive producedure is finally suggested to improve the dynamic range of these systems.

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Call number: LD2668 .T4 1963 W66 / Master of Science

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / This paper discusses channel simulation using OPNET Modeler in support of iNET. It shows how wireless communication is simulated, how to simulate the special aeronautical channel of iNET, and how to deliver the aeronautical channel, test article, and ground station as reusable components for future simulation. Network simulation is a critical tool for iNET and it enables design decisions that cannot be made analytically due to the complexity of the problem. This work addresses the incorporation of the aeronautical channel into the OPNET Modeler tool set as this piece of iNET is unique and is not available in OPNET Modeler.

Bayesian analysis is definitely different from the classic statistical methods. Although, both of them use subjective ideas, it is used in the selection of models in the classic statistical methods, rather than as an explicit part in Bayesian models, which allows the combination of subjective ideas with the data collected, update the prior information and improve inferences. Drastic growth of Bayesian applications indicates it becomes more and more popular, because the advent of computational methods (e.g., MCMC) renders sophisticated analysis. In Bayesian framework, the flexibility and generality allows it to cope with very complex problems. One big obstacle in earlier Bayesian analysis is how to sample from the usually complex posterior distribution. With modern techniques and fast-developed computation capacity, we now have tools to solve this problem. We discuss Acceptance-Rejection sampling, importance sampling and then the MCMC methods. Metropolis-Hasting algorithm, as a very versatile, efficient and powerful simulation technique to construct a Markov Chain, borrows the idea from the well-known acceptance-rejection sampling to generate candidates that are either accepted or rejected, but then retains the current values when rejection takes place (1). A special case of Metropolis-Hasting algorithm is Gibbs Sampler. When dealing with high dimensional problems, Gibbs Sampler doesn’t require a decent proposal distribution. It generates the Markov Chain through univariate conditional probability distribution, which greatly simplifies problems. We illustrate the use of those approaches with examples (with R codes) to provide a thorough review. Those basic methods have variants to deal with different situations. And they are building blocks for more advanced problems. This report is not a tutorial for statistics or the software R. The author assumes that readers are familiar with basic statistical concepts and common R statements. If needed, a detailed instruction of R programming can be found in the Comprehensive R Archive Network (CRAN): http://cran.R-project.org / text

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