MiniCurso Bayesian Computing with INLA
Prof. Havard Rue - Department of Mathematical Sciences - Norwegian University of Science and Technology (www.math.ntnu.no/~hrue)
- FCUL - FUNDAÇÃO FCUL - Piso 3 Anfiteatro - 9h -12:30h e 14h - 17:30h
- Segunda-feira, 8 de Novembro de 2010
Resumo: In these lectures, we discuss approximate Bayesian inference for a class of models named `latent Gaussian models' (LGM). LGM's are perhaps the most commonly used class of models in statistical applications. It includes, among others, most of (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. The concept of LGM is intended for the modeling stage, but turns out to be extremely usefull when doing inference as we can treat models listed above in a unified way and using the *same* algorithm and software tool. Our approach to (approximate) Bayesian inference is to use integrated nested Laplace approximations (INLA). Using this new tool, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged. We introduce the required background and theory for understanding INLA, including details on Gaussian Markov random fields and fast computations of those using sparse matrix algorithms. We end these lectures illustrating INLA on a range of examples in R (see www.r-inla.org).
CV: Håvard Rue é professor de Estatística no "Department of Mathematical Sciences - Norwegian University of Science and Technology". As suas áreas de investigação são simulação estocástica, métodos MCMC, Estatística espacial e inferência bayesiana tendo actuado como editor associado nas seguintes revistas: Journal of the Royal Statistical Society B, Scandinavian Journal of Statistics, Annals of Statistics, Statstics Surveys e Environmetrics. Entre outras publicações, destacam-se o livro "Gaussian Markov Random Fields: Theory and Applications" (2005, Chapman & Hall/CRC) e o artigo "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations" (2009, JRRS-B 71, pg. 319-392).
Público-alvo: Estudantes de doutoramento e investigadores em Estatística que querem compreender e aplicar métodos bayesianos aproximados para modelos gaussianos latentes.
Data: 08 de Novembro de 2010 (9:00-12:30 e 14:00-17:30)
Local: Faculdade de Ciências da Universidade de Lisboa (Fundação FCUL)
Pré-inscrições: 14-25 de Setembro de 2010
Preço do minicurso: 50 Euros
Contacto: Margarida Silva (CEAUL) - Email: email@example.com - Tel. 217 500 120