Comparar métodos

	Regresión Bayesiana Binomial Negativa	Regresión Binomial Negativa
Campo	Estadística	Econometría
Familia	Regression model	Regression model
Año de origen	1990s–2000s	2011
Autor original	Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi	Hilbe (textbook treatment); generalized linear model framework
Tipo	Bayesian GLM for overdispersed counts	Generalized linear model for count data
Fuente 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	Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.
Alias	Bayesian NB regression, Bayesian negbin model, Bayesian overdispersed count regression, Bayesian NB-2 model	NB regression, NB2 regression, negatif binom regresyonu
Relacionados	6	4
Resumen	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.	Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.