পদ্ধতির তুলনা করুন

	বেয়েশীয় ননপ্যারামেট্রিক পদ্ধতিসমূহ	গাউসিয়ান প্রসেস
ক্ষেত্র	বেইসীয়	যন্ত্র শিখন
পরিবার	Bayesian methods	Machine learning
উদ্ভবের বছর	1973 (DP); 2006 (GP canonical text)	2006 (book); roots in Kriging, 1951)
প্রবর্তক	Ferguson (Dirichlet Process, 1973); Rasmussen & Williams (GP, 2006)	Rasmussen, C. E. & Williams, C. K. I.
ধরন	Bayesian nonparametric model	Probabilistic non-parametric model
মৌলিক উৎস	Rasmussen, C.E. & Williams, C.K.I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0262182539	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
অপর নাম	BNP, Dirichlet process mixture, DPM, Gaussian process regression	GP, Gaussian Process Regression, GPR, Kriging
সম্পর্কিত	3	3
সারসংক্ষেপ	Bayesian nonparametric methods are a family of flexible Bayesian models in which model complexity is not fixed in advance but grows automatically with the data. The two most widely used members are the Dirichlet Process Mixture (DPM), which clusters observations without pre-specifying the number of clusters, and Gaussian Process (GP) regression, which places a prior directly over functions and performs regression or classification without committing to a parametric form. Both frameworks were formalised in the Bayesian nonparametric literature, with the canonical GP treatment given by Rasmussen and Williams (2006).	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.