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	बायेसियन प्रोबिट मॉडल	बायेसियन ऑर्डिनल लॉजिस्टिक रिग्रेशन
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model
उद्भव वर्ष	1993	1999
प्रवर्तक	Albert & Chib (data augmentation formulation)	Johnson & Albert (1999); Bayesian proportional odds framework
प्रकार	Binary regression (Bayesian)	Bayesian generalized linear model
मौलिक स्रोत	Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669-679.	Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484
उपनाम	Bayesian probit regression, probit model with data augmentation, Gibbs sampling probit, Albert-Chib probit	Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model
संबंधित	6	6
सारांश	The Bayesian Probit model is a binary regression method that models the probability of a binary outcome using the normal CDF (probit link) within a Bayesian framework. It assigns prior distributions to regression coefficients and updates them with observed data, yielding a full posterior distribution rather than a single point estimate. The Albert-Chib data-augmentation algorithm makes posterior sampling computationally efficient via Gibbs sampling.	Bayesian ordinal logistic regression extends the classical proportional odds model by placing prior distributions on the regression coefficients and threshold parameters and updating them with observed data via Bayes' theorem. The result is a full posterior distribution over all parameters, enabling uncertainty quantification without relying on large-sample approximations.