Comparar métodos

	Modelo Tobit Bayesiano	Modelo Lineal Generalizado Bayesiano
Campo	Estadística	Estadística
Familia	Regression model	Regression model
Año de origen	1958 (classical); 1992 (Bayesian formulation)	1989 (GLM); 1995 (Bayesian BDA)
Autor original	James Tobin (classical Tobit, 1958); Siddhartha Chib (Bayesian Tobit, 1992)	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Tipo	Bayesian censored/limited-dependent-variable regression	Bayesian regression model
Fuente seminal	Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26(1), 24–36.	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
Alias	Bayesian censored regression, Bayesian Type I Tobit, Bayesian truncated regression, Tobit with priors	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
Relacionados	5	6
Resumen	The Bayesian Tobit model extends Tobin's censored regression framework by replacing maximum-likelihood point estimates with a full posterior distribution over regression coefficients and error variance. By embedding Gibbs sampling with data augmentation, it produces credible intervals, handles small censored samples gracefully, and naturally incorporates prior knowledge about effect sizes.	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.