Comparer des méthodes

	Régression binomiale négative bayésienne	Modèle bayésien linéaire généralisé
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1990s–2000s	1989 (GLM); 1995 (Bayesian BDA)
Auteur d'origine	Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Type	Bayesian GLM for overdispersed counts	Bayesian regression model
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	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
Alias	Bayesian NB regression, Bayesian negbin model, Bayesian overdispersed count regression, Bayesian NB-2 model	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
Apparentées	6	6
Résumé	Bayesian Negative Binomial Regression models non-negative integer count outcomes that exhibit overdispersion — where the variance exceeds the mean — by placing a negative binomial likelihood on the data and specifying prior distributions over the regression coefficients and the dispersion parameter. Posterior inference is typically performed via Markov chain Monte Carlo (MCMC) or variational methods, yielding full posterior distributions rather than point estimates.	A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome.