方法对比

	贝叶斯线性回归	Bayesian Regression
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	2013 (modern reference); foundations 18th–19th century	—
提出者	Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al.	—
类型	Bayesian linear model	Bayesian linear model
开创性文献	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	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
别名	bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon	bayesian linear regression, probabilistic regression, bayesian regresyon
相关	4	2
摘要	Bayesian linear regression is a probabilistic extension of the ordinary linear model, introduced through Bayes' rule and formalised in its modern computational workflow by Gelman et al. (2013). Rather than returning a single point estimate for each coefficient, it combines a user-specified prior distribution with the likelihood of the observed data to produce a full posterior distribution over all parameters, from which credible intervals and posterior predictive distributions are derived.	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.