Salīdzināt metodes

	Bayesiešu modelis ar pārmērīgu nulles vērtību skaitu	Modelis ar pārmērīgu nulles vērtību skaitu
Nozare	Statistika	Statistika
Saime	Regression model	Regression model
Izcelsmes gads	1992–2006	1992
Autors	Lambert (1992) for ZIP; Bayesian extension by Ghosh, Mukhopadhyay & Lu (2006)	Diane Lambert
Tips	Bayesian count regression	Count regression with excess zeros
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.	Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14.
Citi nosaukumi	Bayesian ZIP, Bayesian ZINB, Bayesian zero-inflated Poisson, Bayesian zero-inflated negative binomial	ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial
Saistītās	5	6
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.	A zero-inflated model is a two-component mixture regression designed for count outcomes that contain more zero values than a standard Poisson or negative binomial distribution can accommodate. One component is a binary process that generates structural zeros; the other is a count process that generates both zeros and positive counts.