So sánh phương pháp

	Nghiên cứu sinh thái Bayes	Nghiên cứu đoàn hệ Bayes
Lĩnh vực	Dịch tễ học	Dịch tễ học
Họ	Process / pipeline	Process / pipeline
Năm ra đời	1991–2000s (Besag 1991 for spatial priors; Lawson 2001 for disease mapping framework)	1990s–2000s (widespread adoption in epidemiology)
Người khởi xướng	Andrew Lawson; Julian Besag (spatial Bayesian foundations)	Bayesian framework: Thomas Bayes / Pierre-Simon Laplace; applied to cohort epidemiology from the 1990s onward
Loại	Observational epidemiological design with Bayesian statistical framework	Observational longitudinal study with Bayesian inference
Công trình gốc	Lawson, A. B. (2013). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (2nd ed.). CRC Press. ISBN: 978-1466504813	Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Wiley. ISBN: 978-0471499756
Tên gọi khác	Bayesian ecological analysis, Bayesian disease mapping, Bayesian ecological regression, Bayesian spatial ecological study	Bayesian longitudinal cohort, Bayesian prospective cohort, Bayesian cohort analysis, Bayesian follow-up study
Liên quan	3	5
Tóm tắt	A Bayesian ecological study combines the group-level observational design of classical ecological epidemiology with Bayesian hierarchical modelling. Rather than treating disease rates as fixed quantities, it places prior distributions over latent spatial or temporal effects — commonly using the Besag-York-Mollié (BYM) convolution prior — and updates beliefs from aggregate data to produce posterior maps of disease risk, smoothed rate estimates, and credible intervals for ecological associations between exposures and outcomes.	A Bayesian cohort study follows a defined group of individuals over time to estimate incidence, risk, or rate of outcomes, while using Bayesian statistical inference to incorporate prior knowledge and quantify uncertainty through posterior probability distributions rather than classical p-values and confidence intervals. It combines the longitudinal observational design of a cohort study with the probability-updating logic of Bayesian analysis, allowing richer uncertainty quantification and sequential updating as data accumulate.