[ABE-L] Seminarios em High Dimensional Data Analysis

[Ronaldo Dias]

Prezados(as),

Continuando com os seminários em <AD,AD> teremos esta semana.

Seminários em High Dimensional Data Analysis.
Data: 8/05/2019
Local: sala 221, IMECC

Title: INLA, a Computationally Efficient Method for Gaussian Latent Models.
Leonardo U. Pedreira, IMECC-Unicamp

Abstract:
Latent Gaussian Models is a broad class of widely used statistical models
which includes GLM(M), GAM(M), State-space Models, spatial/spatial-temporal
models and more. However, the high dimensional aspect of modern problems
imposes some computational constraints to the classic MCMC paradigm for
parameter estimation. In this brief talk the aim is to introduce the INLA
(Integrated Nested Laplace Approximation) Method as an alternative way to
estimate the latent field.

Paper:

Håvard Rue, Sara Martino and Nicolas Chopin. (2009) Approximate Bayesian
Inference for Latent Gaussian Models by using the Integrated Nested Laplace
Approximations; JRSS.


-- 
Ronaldo Dias, Ph.D.
Professor
Dept. of Statistics-IMECC, UNICAMP
www.ime.unicamp.br/~dias
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