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	Bayesiansk negativ binomialregression	Poisson- och negativ binomialregression
Ämnesområde	Statistik	Ekonometri
Familj	Regression model	Regression model
Ursprungsår	1990s–2000s	1998
Upphovsperson	Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Typ	Bayesian GLM for overdispersed counts	Generalized linear model for count data
Ursprungskälla	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 NB regression, Bayesian negbin model, Bayesian overdispersed count regression, Bayesian NB-2 model	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Närliggande	6	4
Sammanfattning	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.	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.