手法を比較

	ベイジアン・ガウス過程	ベイズ線形回帰	ガウス過程
分野	機械学習	ベイズ	機械学習
系統	Machine learning	Bayesian methods	Machine learning
提唱年	1978–2006	2013 (modern reference); foundations 18th–19th century	2006 (book); roots in Kriging, 1951)
提唱者	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.	Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al.	Rasmussen, C. E. & Williams, C. K. I.
種類	Probabilistic kernel model	Bayesian linear model	Probabilistic non-parametric model
原典	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	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	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
別名	GP regression, GPR, Gaussian process model, GP classifier	bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon	GP, Gaussian Process Regression, GPR, Kriging
関連	3	4	3
概要	A Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning.	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.	A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.