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Title: | Comparing MCMC and INLA for disease mapping with Bayesian hierarchical models | Authors: | De Smedt, Tom Simons, Koen Van Nieuwenhuyse, An MOLENBERGHS, Geert |
Issue Date: | 2016 | Source: | Archives of public health, 73(Suppl 1), p. 1-1 | Abstract: | 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. | Document URI: | http://hdl.handle.net/1942/25222 | Link to publication/dataset: | https://doi.org/10.1186/2049-3258-73-S1-O2 | ISSN: | 0778-7367 | e-ISSN: | 2049-3258 | DOI: | 10.1186/2049-3258-73-S1-O2 | Rights: | © 2015 De Smedt et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. | Category: | A2 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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