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| مؤشر مورَان البايزي× | الانحدار المكاني البيزي× | |
|---|---|---|
| المجال | التحليل المكاني | التحليل المكاني |
| العائلة | Regression model | Regression model |
| سنة النشأة≠ | 1950 / 2000s | 1990s–2000s |
| صاحب الطريقة≠ | Moran (1950), Bayesian extension developed in spatial statistics literature (late 1990s–2000s) | Banerjee, Carlin & Gelfand (foundational treatment); building on Besag (1974) for lattice priors |
| النوع≠ | Bayesian spatial autocorrelation test | Bayesian hierarchical regression |
| المصدر التأسيسي≠ | Haining, R. (2003). Spatial Data Analysis: Theory and Practice. Cambridge University Press. ISBN: 9780521774611 | Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173 |
| الأسماء البديلة | Bayesian spatial autocorrelation test, Bayesian Moran statistic, Moran's I under Bayesian inference, Bayesian global spatial association | Bayesian hierarchical spatial model, BSR, Bayesian geostatistical regression, Bayesian spatial linear model |
| ذات صلة≠ | 6 | 3 |
| الملخص≠ | Bayesian Moran's I embeds the classical Moran's I spatial autocorrelation test within a Bayesian probabilistic framework. Rather than producing a single p-value, it yields a posterior distribution over the spatial autocorrelation parameter, enabling uncertainty quantification, incorporation of prior knowledge, and more principled inference in small or irregular spatial datasets. | 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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