Contributions computationnelles à la statistique Bayésienne
AbstractThis thesis presents contributions to the Monte Carlo methodology used in Bayesian statistics. The Bayesian framework is one of the main approaches to statistics and includes a rich methodology to perform inference and model choice. However, as statistical models become more realistic and drift away from the classical assumptions of normality and linearity, computing some of the quantities involved in the statistical analysis becomes a challenge in itself. In particular high-dimensional integrals have to be efficiently approximated, where the integrands can be highly multimodal. Moreover each point-wise evaluation of the integrands can require a lot of computational effort, which results in expensive integration schemes. These integrals are typically approximated using Monte Carlo methods, requiring the ability to sample from general probability distributions. The first chapter of this document explains this motivating context and reviews some of the most generic Monte Carlo techniques. The following chapters aim at improving some of these techniques, at proposing new methods and at analysing their theoretical properties, in the context of sampling from multimodal and computationally expensive probability distributions.
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Bibliographic InfoThis book is provided by Paris Dauphine University in its series Economics Thesis from University Paris Dauphine with number 123456789/12804 and published in 2012.
bayesian statistics; Monte Carlo methodology;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Nicolas Chopin, 2000.
"A Sequential Particle Filter Method for Static Models,"
2000-45, Centre de Recherche en Economie et Statistique.
- Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
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