পদ্ধতির তুলনা করুন

	বেয়েশীয় অর্ডিনাল লজিস্টিক রিগ্রেশন	বেয়েশীয় বহুপদী লজিস্টিক রিগ্রেশন
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1999	1966 (classical); Bayesian extensions established by 1990s
প্রবর্তক	Johnson & Albert (1999); Bayesian proportional odds framework	Gelman et al. (Bayesian treatment); classical multinomial logit by Cox (1966)
ধরন	Bayesian generalized linear model	Bayesian classification model
মৌলিক উৎস	Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484	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
অপর নাম	Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model	Bayesian polytomous logistic regression, Bayesian multinomial logit, Bayesian softmax regression, Bayesian nominal logistic regression
সম্পর্কিত	6	5
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.	Bayesian Multinomial Logistic Regression models a nominal outcome with three or more unordered categories by placing prior distributions over the regression coefficients and updating them with data via Bayes' theorem. The result is a full posterior distribution over category probabilities for each observation, enabling principled uncertainty quantification and regularization through the prior.