Salīdzināt metodes

	Bayesiešu modelis ar pārmērīgu nulles vērtību skaitu	Puasona un negatīvās binomiālās regresijas
Nozare	Statistika	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1992–2006	1998
Autors	Lambert (1992) for ZIP; Bayesian extension by Ghosh, Mukhopadhyay & Lu (2006)	Cameron & Trivedi (textbook treatment); Hilbe (negative binomial)
Tips	Bayesian count regression	Generalized linear model for count data
Pirmavots	Ghosh, S. K., Mukhopadhyay, P., & Lu, J.-C. (2006). Bayesian analysis of zero-inflated regression models. Journal of Statistical Planning and Inference, 136(4), 1360–1375.	Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press.
Citi nosaukumi	Bayesian ZIP, Bayesian ZINB, Bayesian zero-inflated Poisson, Bayesian zero-inflated negative binomial	count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon
Saistītās	5	4
Kopsavilkums	The Bayesian zero-inflated model handles count data with excess zeros by combining a binary component — identifying structural zeros — with a count component (Poisson or negative binomial) for the remaining counts. Bayesian inference via MCMC provides full posterior distributions for all parameters, enabling principled uncertainty quantification and regularisation through priors.	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.