مقایسهٔ روش‌ها

	رگرسیون بیزی	رگرسیون لجستیک
حوزه	بیزی	آمار پژوهش
خانواده	Bayesian methods	Process / pipeline
سال پیدایش	—	1958
پدیدآور	—	David Roxbee Cox
نوع	Bayesian linear model	Method
منبع بنیادین	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	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
نام‌های دیگر	bayesian linear regression, probabilistic regression, bayesian regresyon	logit model, binomial logistic regression, LR
مرتبط	2	3
خلاصه	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.