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| Байесова оценка на плътността с ядро× | Байесовски пространствен регресионен модел× | |
|---|---|---|
| Област | Пространствен анализ | Пространствен анализ |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1995 | 1990s–2000s |
| Създател≠ | Hjort & Glad (1995); extended by various authors in Bayesian nonparametrics | Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors |
| Тип≠ | Nonparametric density estimation | Bayesian hierarchical regression |
| Основополагащ източник≠ | Hjort, N. L., & Glad, I. K. (1995). Nonparametric density estimation with a parametric start. The Annals of Statistics, 23(3), 882–904. DOI ↗ | Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173 |
| Други названия | Bayesian KDE, BKDE, Bayesian nonparametric density estimation, Bayesian adaptive KDE | Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model |
| Свързани≠ | 5 | 3 |
| Резюме≠ | 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. |
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