Methoden vergleichen

	Bayesian Kernel Density Estimation	Bayesian Kriging (Modellbasierte Geostatistik)
Fachgebiet	Räumliche Analyse	Räumliche Analyse
Familie	Regression model	Regression model
Entstehungsjahr	1995	1993–1998
Urheber	Hjort & Glad (1995); extended by various authors in Bayesian nonparametrics	Diggle, Tawn & Moyeed; Handcock & Stein
Typ	Nonparametric density estimation	Bayesian spatial interpolation
Wegweisende Quelle	Hjort, N. L., & Glad, I. K. (1995). Nonparametric density estimation with a parametric start. The Annals of Statistics, 23(3), 882–904.	Diggle, P. J., Tawn, J. A., & Moyeed, R. A. (1998). Model-based geostatistics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(3), 299–350.
Aliasnamen	Bayesian KDE, BKDE, Bayesian nonparametric density estimation, Bayesian adaptive KDE	Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging
Verwandt	5	5
Zusammenfassung	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 Kriging embeds classical geostatistical interpolation inside a full probabilistic framework. Instead of treating variogram parameters as fixed point estimates, it places prior distributions on them and updates these priors with observed spatial data to obtain a posterior distribution. Predictions at unsampled locations are then marginalised over this uncertainty, yielding honest predictive intervals that account for both spatial dependence and parameter uncertainty.