Review
Bayesian regression modeling with INLA. Xiaofeng Wang, Yu R. Yue and Julian J. Faraway. Boca Raton: CRC Press
Biometrics, v 75(3), pp 1042-1044
Sep 2019
Abstract
The Bayesian statistical framework has a long history of being applied to a variety of disciplines and fields, ranging from medicine and population health to financial forecasting—owing to the development of computational approaches to implement it, such as Markov Chain Monte Carlo. Despite its many advantages, the time and computational burden required to achieve estimates of interest can be unreasonable, particularly when fitting complex models. Computational algorithm and software development have attempted to resolve the computational burden for Bayesian inference (Lunn et al., 2000), including the development of the integrated nested Laplace approximation (INLA) method. [1st paragraph]
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Details
- Title
- Bayesian regression modeling with INLA. Xiaofeng Wang, Yu R. Yue and Julian J. Faraway. Boca Raton: CRC Press
- Creators
- Loni P. Tabb - Drexel University
- Publication Details
- Biometrics, v 75(3), pp 1042-1044
- Publisher
- Wiley
- Number of pages
- 2
- Resource Type
- Review
- Language
- English
- Academic Unit
- Urban Health Collaborative; Epidemiology and Biostatistics
- Other Identifier
- 991021011844704721