Salīdzināt metodes

	Neiguatnā Binomiālā Regresija (Bayesian Negative Binomial Regression)	Negatīvā binomiālā regresija
Nozare	Statistika	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1990s–2000s	2011
Autors	Gelman, Carlin, Stern, Dunson, Vehtari & Rubin; Cameron & Trivedi	Hilbe (textbook treatment); generalized linear model framework
Tips	Bayesian GLM for overdispersed counts	Generalized linear model for count data
Pirmavots	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.
Citi nosaukumi	Bayesian NB regression, Bayesian negbin model, Bayesian overdispersed count regression, Bayesian NB-2 model	NB regression, NB2 regression, negatif binom regresyonu
Saistītās	6	4
Kopsavilkums	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.