Linganisha mbinu

	Utafsiri wa Kina wa Msingi wa Kibanzi cha Bayesian	Regressioni za Bayesian za Angani
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1995	1990s–2000s
Mwanzilishi	Hjort & Glad (1995); extended by various authors in Bayesian nonparametrics	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors
Aina	Nonparametric density estimation	Bayesian hierarchical regression
Chanzo asilia	Hjort, N. L., & Glad, I. K. (1995). Nonparametric density estimation with a parametric start. The Annals of Statistics, 23(3), 882–904.	Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173
Majina mbadala	Bayesian KDE, BKDE, Bayesian nonparametric density estimation, Bayesian adaptive KDE	Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model
Zinazohusiana	5	3
Muhtasari	Bayesian Kernel Density Estimation (BKDE) is a nonparametric method for estimating the probability density function of a spatial or attribute variable by combining a kernel smoother with a Bayesian prior over the bandwidth parameter. The posterior distribution of the bandwidth propagates uncertainty into the final density estimate rather than treating the bandwidth as a fixed tuning constant.	Bayesian Spatial Regression embeds a spatially structured random effect into a regression framework and estimates all parameters — including spatial range and variance — through posterior inference rather than point estimation. It handles spatial autocorrelation, quantifies full predictive uncertainty, and accommodates small or irregular spatial datasets via hierarchical priors.