Výsledky vyhledávání - "Martins, Thiago G."

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Penalising model component complexity: A principled, practical approach to constructing priors

Autor: Simpson, Daniel P., Rue, Håvard, Martins, Thiago G., Riebler, Andrea, Sørbye, Sigrunn H.

In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base model. Proper priors are

Externí odkaz: http://arxiv.org/abs/1403.4630

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Sensitivity analysis for Bayesian hierarchical models

Autor: Roos, Malgorzata, Martins, Thiago G., Held, Leonhard, Rue, Havard

Prior sensitivity examination plays an important role in applied Bayesian analyses. This is especially true for Bayesian hierarchical models, where interpretability of the parameters within deeper layers in the hierarchy becomes challenging. In addit

Externí odkaz: http://arxiv.org/abs/1312.4797

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Extending INLA to a class of near-Gaussian latent models

Autor: Martins, Thiago G., Rue, Håvard

This work extends the Integrated Nested Laplace Approximation (INLA) method to latent models outside the scope of latent Gaussian models, where independent components of the latent field can have a near-Gaussian distribution. The proposed methodology

Externí odkaz: http://arxiv.org/abs/1210.1434

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Bayesian computing with INLA: new features

Autor: Martins, Thiago G., Simpson, Daniel, Lindgren, Finn, Rue, Håvard

The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice via the R-package R-INLA. In this paper we formalize

Externí odkaz: http://arxiv.org/abs/1210.0333

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