পদ্ধতির তুলনা করুন

	বেয়েশীয় বহুপদী লজিস্টিক রিগ্রেশন	মাল্টিনোমিয়াল লজিস্টিক রিগ্রেশন
ক্ষেত্র	পরিসংখ্যান	পরিসংখ্যান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1966 (classical); Bayesian extensions established by 1990s	1966–1974
প্রবর্তক	Gelman et al. (Bayesian treatment); classical multinomial logit by Cox (1966)	Cox (1966); Theil (1969); formalized by McFadden (1974)
ধরন	Bayesian classification model	Generalized linear model
মৌলিক উৎস	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	Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933
অপর নাম	Bayesian polytomous logistic regression, Bayesian multinomial logit, Bayesian softmax regression, Bayesian nominal logistic regression	polytomous logistic regression, softmax regression, multinomial logit, nominal logistic regression
সম্পর্কিত	5	4
সারসংক্ষেপ	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.	Multinomial logistic regression extends binary logistic regression to outcomes with three or more unordered categories. It models the log-odds of each category relative to a chosen reference category as a linear function of the predictors, and estimates all parameters simultaneously via maximum likelihood. It is the standard choice when the dependent variable is nominal with multiple levels.