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.
Highlights
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)
Integrated Nested Laplace Approximation was developed as an alternative method to fit Bayesian hierarchical models of the latent Gaussian class
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Summary
Comparing MCMC and INLA for disease mapping with Bayesian hierarchical models Tom De Smedt1*, Koen Simons[2], An Van Nieuwenhuyse[2], Geert Molenberghs[3]
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