Compara mètodes

	Regressió Bayesiana Binomial Negativa	Model Lineal General Bayesiana
Camp	Estadística	Estadística
Família	Regression model	Regression model
Any d'origen	1990s–2000s	1989 (GLM); 1995 (Bayesian BDA)
Autor original	Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Tipus	Bayesian GLM for overdispersed counts	Bayesian regression model
Font seminal	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
Àlies	Bayesian NB regression, Bayesian negbin model, Bayesian overdispersed count regression, Bayesian NB-2 model	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
Relacionats	6	6
Resum	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.