Linganisha mbinu

	Usajili wa Bayesian	Mchakato wa Gaussia	Markov Chain Monte Carlo (MCMC)
Nyanja	Mbinu za Bayes	Ujifunzaji wa Mashine	Mbinu za Bayes
Familia	Bayesian methods	Machine learning	Bayesian methods
Mwaka wa asili	—	2006 (book); roots in Kriging, 1951)	—
Mwanzilishi	—	Rasmussen, C. E. & Williams, C. K. I.	—
Aina	Bayesian linear model	Probabilistic non-parametric model	Posterior sampling algorithm
Chanzo asilia	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	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
Majina mbadala	bayesian linear regression, probabilistic regression, bayesian regresyon	GP, Gaussian Process Regression, GPR, Kriging	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Zinazohusiana	2	3	3
Muhtasari	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.	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.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.