Bayesian Hierarchical Models with Applications
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 20 August 2025 | Viewed by 185
Special Issue Editors
Interests: Bayesian hierarchical model research evaluation; socioeconomic disparities and cancer burden; gender gap in academia
Special Issue Information
Dear Colleagues,
Hierarchical Bayesian models have become increasingly widespread thanks to the development of numerous supporting software, starting from BUGS, JAGS, and Stan, all of which have comfortable interface. The interesting aspect lies in how the model is defined, the prior distributions, and the choice of levels in the hierarchical structure. On one hand, their simplicity in interpretation, and on the other, the ease of implementation, make these models highly versatile. They are, in fact, applied across a wide range of fields, from epidemiology to social and environmental sciences.
The use of multilevel models, with particular attention to random effect models, is highly important. Furthermore, a discussion on their definition and, more importantly, how to interpret the posterior estimations would greatly benefit the statistical community.
Prof. Dr. Maura Mezzetti
Prof. Dr. Ilia Negri
Guest Editors
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Keywords
- Bayesian inference
- MCMC
- multilevel model
- random effects
- model comparison
- posterior distributions
- Stan
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