Orozco Acosta, ErickAdin Urtasun, AritzUgarte Martínez, María Dolores2022-01-212022-01-212020E. Orozco-Acosta, A. Adin and M. D. Ugarte, 'Bayesian Modeling Approach in Big Data Contexts: an Application in Spatial Epidemiology', 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020, pp. 749-750, doi: 10.1109/DSAA49011.2020.00097.978-1-7281-8206-32472-157310.1109/DSAA49011.2020.00097https://academica-e.unavarra.es/handle/2454/41877In this work we propose a novel scalable Bayesian modeling approach to smooth mortality risks borrowing information from neighbouring regions in high-dimensional spatial disease mapping contexts. The method is based on the well-known divide and conquer approach, so that the spatial domain is divided into D subregions where local spatial models can be fitted simultaneously. Model fitting and inference has been carried out using the integrated nested Laplace approximation (INLA) technique. Male colorectal cancer mortality data in the municipalities of continental Spain have been analyzed using the new model proposals. Results show that the new modeling approach is very competitive in terms of model fitting criteria when compared with a global spatial model, and it is computationally much more efficient.2 p.application/pdfeng© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.Disease mappingHigh-dimensional dataINLAParallel computinginfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess