Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Simulação por Bootstrap Espacial× | Filtro de Kalman× | |
|---|---|---|
| Área | Bayesiano | Bayesiano |
| Família | Bayesian methods | Bayesian methods |
| Ano de origem≠ | 1990s–2000s | 1960 |
| Autor original≠ | Lahiri and others, building on Efron's bootstrap (1979) | Rudolf E. Kalman |
| Tipo≠ | Resampling / simulation | recursive Bayesian filter |
| Fonte seminal≠ | Lahiri, S. N. (2003). Resampling Methods for Dependent Data. Springer. ISBN: 978-0387009285 | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI ↗ |
| Outros nomes | spatial block bootstrap, spatial resampling, geostatistical bootstrap, bootstrap for spatial data | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter |
| Relacionados≠ | 4 | 5 |
| Resumo≠ | Spatial bootstrap simulation is a resampling technique designed for spatially dependent data. By resampling contiguous spatial blocks rather than independent observations, it preserves the local autocorrelation structure of the data and yields valid estimates of sampling variability for statistics computed on geographic or lattice observations. | The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time. |
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