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| 贝叶斯核密度估计× | Bayesian Spatial Regression× | |
|---|---|---|
| 领域 | 空间分析 | 空间分析 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1995 | 1990s–2000s |
| 提出者≠ | Hjort & Glad (1995); extended by various authors in Bayesian nonparametrics | Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors |
| 类型≠ | Nonparametric density estimation | Bayesian hierarchical regression |
| 开创性文献≠ | 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 |
| 别名 | Bayesian KDE, BKDE, Bayesian nonparametric density estimation, Bayesian adaptive KDE | Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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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