Defesa de Dissertação de Mestrado: Exploring Tolerance Selection and Multifidelity Techniques in an Approximate Bayesian Computation Framework

Orientadores:
Renato Simões Silva - Laboratório Nacional de Computação Científica - LNCC
Regina Célia Cerqueira de Almeida - Laboratório Nacional de Computação Científica - LNCC

Banca Examinadora:
Renato Simões Silva - Laboratório Nacional de Computação Científica - LNCC (presidente)
Marcio Rentes Borges - Laboratório Nacional de Computação Científica - LNCC
Alvaro Luiz Gayoso de Azeredo Coutinho - Universidade Federal do Rio de Janeiro - COPPE/UFRJ
Wellington Betencurte da Silva - UFES

Suplentes:
Marcelo Trindade dos Santos - Laboratório Nacional de Computação Científica - LNCC

Resumo:

Statistical inference is essential for many scientific pr oblems. Simulating parameter values or the models that generated a given data set is often a complex task. The Bayesian paradigm offers a flexible and robust framework for addressing these issues. In many instances, Bayesian inference necessitates the computation of the likelihood function, which represents the probability of obtaining the observed data given a set of parameter values.
However, calculating the likelihood function can be extremely challenging.
There is a diverse array of tools dedicated to parameter estimation and model selection processes. One category of inference techniques that operates independently of the likelihood is known as approximate Bayesian computation (ABC), which eliminates the need to explicitly calculate a likelihood function. The fundamental premise of ABC methods is to provide an approximation of the posterior distribution using samples drawn from the prior distribution. However, ABC algorithms are computationally expensive due to the requirement for a large
number of model simulations. This work focuses on the analysis of tolerance techniques and the application of the multifidelity process to achieve computational gains without compromising accuracy, utilizing the ABC methodology in epidemiological problems.