পদ্ধতির তুলনা করুন

	বেয়েশীয় অর্ডিনাল লজিস্টিক রিগ্রেশন	বেয়েশীয় প্রোবিট মডেল
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1999	1993
প্রবর্তক	Johnson & Albert (1999); Bayesian proportional odds framework	Albert & Chib (data augmentation formulation)
ধরন	Bayesian generalized linear model	Binary regression (Bayesian)
মৌলিক উৎস	Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484	Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669-679.
অপর নাম	Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model	Bayesian probit regression, probit model with data augmentation, Gibbs sampling probit, Albert-Chib probit
সম্পর্কিত	6	6
সারসংক্ষেপ	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.	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.