Comparer des méthodes

	Régression de Poisson bayésienne	Régression de Poisson et binomiale négative
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1989 (GLM foundation); Bayesian treatment formalized in 1990s–2000s	1998
Auteur d'origine	Gelman et al. (BDA); classical Poisson GLM from McCullagh & Nelder (1989)	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Type	Bayesian generalized linear model for count data	Generalized linear model for count data
Source fondatrice	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Alias	Bayesian log-linear count model, Bayesian GLM Poisson, Poisson regression with priors, Bayesian count regression	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Apparentées	6	4
Résumé	Bayesian Poisson regression models non-negative integer count outcomes using a Poisson likelihood with a log link, placing prior distributions on the regression coefficients. Posterior inference — combining prior beliefs with the data likelihood — produces full probability distributions over the coefficients rather than single-point estimates, enabling coherent uncertainty quantification and incorporation of domain knowledge.	Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred.