Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Bayesilik tuumtiheduse hindamine× | Bayesian Kriging (Mudelit-põhine geostaistika)× | |
|---|---|---|
| Valdkond | Ruumianalüüs | Ruumianalüüs |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 1995 | 1993–1998 |
| Looja≠ | Hjort & Glad (1995); extended by various authors in Bayesian nonparametrics | Diggle, Tawn & Moyeed; Handcock & Stein |
| Tüüp≠ | Nonparametric density estimation | Bayesian spatial interpolation |
| Algallikas≠ | Hjort, N. L., & Glad, I. K. (1995). Nonparametric density estimation with a parametric start. The Annals of Statistics, 23(3), 882–904. DOI ↗ | 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. DOI ↗ |
| Rööpnimetused | Bayesian KDE, BKDE, Bayesian nonparametric density estimation, Bayesian adaptive KDE | Bayesian geostatistics, model-based geostatistics, Bayesian spatial interpolation, stochastic kriging |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | 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. |
| ScholarGateAndmestik ↗ |
|
|