Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовская оценка плотности ядра× | Локальный кригинг (кригинг в скользящем окне)× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1995 | 1990 |
| Автор метода≠ | Hjort & Glad (1995); extended by various authors in Bayesian nonparametrics | Haas, T. C. |
| Тип≠ | Nonparametric density estimation | Spatial interpolation (local variant) |
| Основополагающий источник≠ | Hjort, N. L., & Glad, I. K. (1995). Nonparametric density estimation with a parametric start. The Annals of Statistics, 23(3), 882–904. DOI ↗ | Haas, T. C. (1990). Kriging and automated variogram modeling within a moving window. Atmospheric Environment, 24(7), 1759-1769. DOI ↗ |
| Другие названия | Bayesian KDE, BKDE, Bayesian nonparametric density estimation, Bayesian adaptive KDE | moving-window kriging, local kriging interpolation, windowed kriging, neighborhood kriging |
| Связанные≠ | 5 | 3 |
| Сводка≠ | 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. | Local Kriging is a spatially adaptive geostatistical interpolation method that restricts each prediction to a moving neighborhood of nearby observations, fitting a variogram model locally within that window. This allows spatial covariance structure to vary across the study region rather than imposing a single global variogram, making it better suited to large or non-stationary spatial fields. |
| ScholarGateНабор данных ↗ |
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