Comparing MCMC and INLA for disease mapping with Bayesian hierarchical models
نویسندگان
چکیده
منابع مشابه
Comparing MCMC and INLA for disease mapping with Bayesian hierarchical models
Introduction 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,...
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ژورنال
عنوان ژورنال: Archives of Public Health
سال: 2015
ISSN: 2049-3258
DOI: 10.1186/2049-3258-73-s1-o2