手法を比較

	ベイズ型ゼロ過剰モデル	ベイズポアソン回帰
分野	統計学	統計学
系統	Regression model	Regression model
提唱年	1992–2006	1989 (GLM foundation); Bayesian treatment formalized in 1990s–2000s
提唱者	Lambert (1992) for ZIP; Bayesian extension by Ghosh, Mukhopadhyay & Lu (2006)	Gelman et al. (BDA); classical Poisson GLM from McCullagh & Nelder (1989)
種類	Bayesian count regression	Bayesian generalized linear model for count data
原典	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
別名	Bayesian ZIP, Bayesian ZINB, Bayesian zero-inflated Poisson, Bayesian zero-inflated negative binomial	Bayesian log-linear count model, Bayesian GLM Poisson, Poisson regression with priors, Bayesian count regression
関連	5	6
概要	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.	Bayesian Poisson regression models non-negative integer count outcomes using a Poisson likelihood with a log link, placing prior distributions on the regression coefficients. Posterior inference — combining prior beliefs with the data likelihood — produces full probability distributions over the coefficients rather than single-point estimates, enabling coherent uncertainty quantification and incorporation of domain knowledge.