Salīdzināt metodes

	Bayesiešu modelis ar pārmērīgu nulles vērtību skaitu	Beijesas vispārinātais lineārais modelis
Nozare	Statistika	Statistika
Saime	Regression model	Regression model
Izcelsmes gads	1992–2006	1989 (GLM); 1995 (Bayesian BDA)
Autors	Lambert (1992) for ZIP; Bayesian extension by Ghosh, Mukhopadhyay & Lu (2006)	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Tips	Bayesian count regression	Bayesian regression model
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.	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
Citi nosaukumi	Bayesian ZIP, Bayesian ZINB, Bayesian zero-inflated Poisson, Bayesian zero-inflated negative binomial	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
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 Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome.