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Open Access 01-12-2015 | Oral presentation

Comparing MCMC and INLA for disease mapping with Bayesian hierarchical models

Authors: Tom De Smedt, Koen Simons, An Van Nieuwenhuyse, Geert Molenberghs

Published in: Archives of Public Health | Special Issue 1/2015

Excerpt

Bayesian hierarchical models with random effects are one of the most widely used methods in modern disease mapping, as a superior alternative to standardized ratios. These models are traditionally fitted through Markov Chain Monte Carlo sampling (MCMC). Due to the nature of the hierarchical models and random effects, the convergence of MCMC is very slow and unpredictable. Recently, Integrated Nested Laplace Approximation was developed as an alternative method to fit Bayesian hierarchical models of the latent Gaussian class. …

Title: Comparing MCMC and INLA for disease mapping with Bayesian hierarchical models
Authors: Tom De Smedt
Koen Simons
An Van Nieuwenhuyse
Geert Molenberghs
Publication date: 01-12-2015
Publisher: BioMed Central
Published in: Archives of Public Health / Issue Special Issue 1/2015
Electronic ISSN: 2049-3258
DOI: https://doi.org/10.1186/2049-3258-73-S1-O2

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