Precisão de simulações para solução de modelos estocásticos

• Ano de defesa: 2010
• Autor(a) principal: Taschetto, Dione
• Orientador(a): Fernandes, Paulo Henrique Lemelle
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Pontifícia Universidade Católica do Rio Grande do Sul; Porto Alegre
• Palavras-chave em Português: INFORMÁTICA; REDES DE AUTÔMATOS ESTOCÁSTICOS; AVALIAÇÃO DE DESEMPENHO (INFORMÁTICA); SIMULAÇÃO E MODELAGEM EM COMPUTADORES
• Link de acesso: http://hdl.handle.net/10923/1509

The use of Markovian formalisms make possible the use and the computational solution of several systems enabling the prediction and evaluation of their behavior standards. The Stochastic Automata Networks (SAN) formalism provides a compact and modular description for Markovian models. Moreover, SAN is suitable to derive performance indices for systems analysis and interpretation using iterative numerical solutions based on a descriptor and a state space sized probability vector. Depending on the size of the model this operation is computationally onerous and sometimes impracticable. An alternative method to compute indices from a model is simulation, mainly because it simply requires the definition of a pseudorandom generator and transition functions for states that enable the creation of a trajectory. The sampling process can be different for each technique, establishing some rules to collect samples for further statistical analysis. Simulation techniques often demand lots of samples in order to calculate statistically relevant performance indices. This work provides comparisons with accuracy of results from some Markovian models which were obtained from the execution of different simulation techniques. It also proposes a different way to simulate Markovian models by using a Bootstrap-based statistical method to minimize the effect of sample choices. The effectiveness of the proposed method, called Bootstrap simulation, is compared to the numerical solution results for a set of examples described using SAN modeling formalism.