No description available.

Neste trabalho apresentamos condições suficientes para a obtenção de taxas exponenciais de convergência na lei multidimensional dos grandes números para campos aleatórios definidos em R^Z_d. Dentre possíveis aplicações do resultado apresentamos medidas não-gibbsianas e não-FKG (limites de saturaçãoo de processos de estacionamento) e medidas estacionárias originárias de sistemas de partículas (rede com perdas, incluindo o caso onde há interação de longo alcance com cauda pesada). / We describe sufficient conditions for the occurrence of exponential rates of convergence in the multidimensional law of large numbers for random fields in RZd . Non-gibbsian and non-FKG measures from statistical mechanics (jamming limits of RSA models) and IPS (stationary measures of loss networks, including heavy-tail long-range interaction) are indicated as examples where the result applies.

This dissertation contains two parts. The first part introduces the first class of perfect sampling algorithms for the steady-state distribution of multi-server queues in which the arrival process is a general renewal process and the service times are independent and identically distributed (iid); the first-in-first-out FIFO GI/GI/c queue with 2 <= c < 1. Two main simulation algorithms are given in this context, where both of them are built on the classical dominated coupling from the past (DCFTP) protocol. In particular, the first algorithm uses a coupled multi-server vacation system as the upper bound process and it manages to simulate the vacation system backward in time from stationarity at time zero. The second algorithm utilizes the DCFTP protocol as well as the Random Assignment (RA) service discipline. Both algorithms have finite expected termination time with mild moment assumptions on the interarrival time and service time distributions. Our methods are also extended to produce exact simulation algorithms for Fork-Join queues and infinite server systems. The second part presents general principles for the design and analysis of unbiased Monte Carlo estimators in a wide range of settings. The estimators possess finite work-normalized variance under mild regularity conditions. We apply the estimators to various applications including unbiased steady-state simulation of regenerative processes, unbiased optimization in Sample Average Approximations and distribution quantile estimation.

Neste trabalho apresentamos condições suficientes para a obtenção de taxas exponenciais de convergência na lei multidimensional dos grandes números para campos aleatórios definidos em R^Z_d. Dentre possíveis aplicações do resultado apresentamos medidas não-gibbsianas e não-FKG (limites de saturaçãoo de processos de estacionamento) e medidas estacionárias originárias de sistemas de partículas (rede com perdas, incluindo o caso onde há interação de longo alcance com cauda pesada). / We describe sufficient conditions for the occurrence of exponential rates of convergence in the multidimensional law of large numbers for random fields in RZd . Non-gibbsian and non-FKG measures from statistical mechanics (jamming limits of RSA models) and IPS (stationary measures of loss networks, including heavy-tail long-range interaction) are indicated as examples where the result applies.

<p>In this thesis we present a model of the interacting agents on the financial market. The agents are represented by a non-Euclidean random graph, where each agent communicate with another with probability p, and the interaction according to the Ising Model. We investigate properties of the model by direct calculations for small graph sizes, and by perfect simulation for larger graph sizes. We also present a model for asset price variation by using the magnetization of the Ising model.</p>

In this thesis we present a model of the interacting agents on the financial market. The agents are represented by a non-Euclidean random graph, where each agent communicate with another with probability p, and the interaction according to the Ising Model. We investigate properties of the model by direct calculations for small graph sizes, and by perfect simulation for larger graph sizes. We also present a model for asset price variation by using the magnetization of the Ising model.

Nosso objeto de estudo são os sistemas de spins com interações de longo alcance; em particular, estamos interessados em sistemas cuja probabilidade invariante é o modelo de Ising em A^S, onde A = {-1, 1} é o espaço de spins e S = Z^d é o espaço de sítios. Apresentamos dois resultados originais que são consequências da aplicação de algoritmos de simulação perfeita e de acoplamento no contexto da construção deste tipo de sistemas e de suas respectivas probabilidades invariantes. / Our object of interest are spin systems with long-range interactions. As a special case, we are interested in systems whose invariant measure is the Ising model on A^S, where A = {-1, 1} is the space of spins and S = Z^d is the space of sites. We present two original results that are byproducts of the application of Perfect Simulation and Coupling algorithms in the context of the construction of these spin systems and their respective invariant measures.

Nesta tese consideramos uma nova classe de sistemas markovianos de partículas com infinitas componentes interagentes. O sistema representa a evolução temporal dos potenciais de membrana de um conjunto infinito de neurônios interagentes. Provamos a existência e unicidade do processo construindo um pseudo-algoritmo de simulação perfeita e mostrando que este algoritmo roda em um número finito de passos quase certamente. Estudamos também o comportamento do sistema quando consideramos apenas um conjunto finito de neurônios. Neste caso, construímos um procedimento de simulação perfeita para o acoplamento entre o processo limitado a um conjunto finito de neurônios e o processo que considera todos os neurônios do sistema. Como consequência encontramos um limitante superior para a probabilidade de discrepância entre os processos. / We consider a new class of interacting particle systems with a countable number of interacting components. The system represents the time evolution of the membrane potentials of an infinite set of interacting neurons. We prove the existence and uniqueness of the process, by the construction of a perfect simulation procedure. We show that this algorithm is successful, that is, we show that the number of steps of the algorithm is finite almost surely. We also study the behaviour of the system when we consider only a finite number of neurons. In this case, we construct a perfect simulation procedure for the coupling of the process with a finite number of neurons and the process with a infinite number of neurons. As a consequence we obtain an upper bound for the error we make when sampling from a finite set of neurons instead of the infinite set of neurons.

Nosso objeto de estudo são os sistemas de spins com interações de longo alcance; em particular, estamos interessados em sistemas cuja probabilidade invariante é o modelo de Ising em A^S, onde A = {-1, 1} é o espaço de spins e S = Z^d é o espaço de sítios. Apresentamos dois resultados originais que são consequências da aplicação de algoritmos de simulação perfeita e de acoplamento no contexto da construção deste tipo de sistemas e de suas respectivas probabilidades invariantes. / Our object of interest are spin systems with long-range interactions. As a special case, we are interested in systems whose invariant measure is the Ising model on A^S, where A = {-1, 1} is the space of spins and S = Z^d is the space of sites. We present two original results that are byproducts of the application of Perfect Simulation and Coupling algorithms in the context of the construction of these spin systems and their respective invariant measures.

Hierarchical models provide a flexible way of modeling complex behavior. However, the complicated interdependencies among the parameters in the hierarchy make training such models difficult. MCMC methods have been widely used for this purpose, but can often only approximate the necessary distributions. Acceptance-rejection sampling allows for perfect simulation from these often unnormalized distributions by drawing from another distribution over the same support. The efficacy of acceptance-rejection sampling is explored through application to a small dataset which has been widely used for evaluating different methods for inference on hierarchical models. A particular algorithm is developed to draw variates from the posterior distribution. The algorithm is both verified and validated, and then finally applied to the given data, with comparisons to the results of different methods.