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	Stima della densità di probabilità mediante kernel bayesiano	Regressione Spaziale Bayesiana
Campo	Analisi spaziale	Analisi spaziale
Famiglia	Regression model	Regression model
Anno di origine	1995	1990s–2000s
Ideatore	Hjort & Glad (1995); extended by various authors in Bayesian nonparametrics	Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors
Tipo	Nonparametric density estimation	Bayesian hierarchical regression
Fonte seminale	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
Alias	Bayesian KDE, BKDE, Bayesian nonparametric density estimation, Bayesian adaptive KDE	Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model
Correlati	5	3
Sintesi	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.