विधियों की तुलना करें

	एन्सेम्बल गॉसियन प्रोसेस	गॉसियन प्रक्रिया
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2000–2015	2006 (book); roots in Kriging, 1951)
प्रवर्तक	Tresp, V. (committee formulation); Deisenroth, M. P. & Ng, J. W. (distributed formulation)	Rasmussen, C. E. & Williams, C. K. I.
प्रकार	Ensemble of probabilistic surrogate models	Probabilistic non-parametric model
मौलिक स्रोत	Tresp, V. (2000). A Bayesian Committee Machine. Neural Computation, 12(11), 2719–2741.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
उपनाम	Gaussian Process ensemble, GP committee machine, distributed GP, mixture of GPs	GP, Gaussian Process Regression, GPR, Kriging
संबंधित	4	3
सारांश	Ensemble Gaussian Process trains multiple independent GP experts on data subsets or overlapping regions, then combines their posterior predictions — means and variances — into a single probabilistic forecast. This approach retains the calibrated uncertainty estimates of standard GPs while overcoming their O(n³) cubic cost bottleneck, making probabilistic regression practical on datasets with thousands to millions of observations.	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.