Methoden vergleichen

	Bayesian Decision Tree	Gauß-Prozess
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1998	2006 (book); roots in Kriging, 1951)
Urheber	Chipman, H. A.; George, E. I.; McCulloch, R. E.	Rasmussen, C. E. & Williams, C. K. I.
Typ	Bayesian ensemble / tree model	Probabilistic non-parametric model
Wegweisende Quelle	Chipman, H. A., George, E. I., & McCulloch, R. E. (1998). Bayesian CART model search. Journal of the American Statistical Association, 93(443), 935–948.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Aliasnamen	Bayesian CART, BCART, Bayesian tree induction, probabilistic decision tree	GP, Gaussian Process Regression, GPR, Kriging
Verwandt	5	3
Zusammenfassung	Bayesian Decision Tree (Bayesian CART) places a prior distribution over tree structures and leaf parameters, then uses Markov chain Monte Carlo to explore the posterior distribution of trees given data. Instead of a single best tree, it produces a distribution of plausible trees whose predictions are averaged, yielding calibrated uncertainty estimates alongside point predictions.	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.