방법 비교

	베이지안 포아송 회귀	Zero-Inflated Model
분야	통계학	통계학
계열	Regression model	Regression model
기원 연도	1989 (GLM foundation); Bayesian treatment formalized in 1990s–2000s	1992
창시자	Gelman et al. (BDA); classical Poisson GLM from McCullagh & Nelder (1989)	Diane Lambert
유형	Bayesian generalized linear model for count data	Count regression with excess zeros
원전	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	Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14.
별칭	Bayesian log-linear count model, Bayesian GLM Poisson, Poisson regression with priors, Bayesian count regression	ZIP model, ZINB model, zero-inflated Poisson, zero-inflated negative binomial
관련	6	6
요약	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.	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.