search query: @indexterm Prediction theory / total: 137
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| Author: | Rose, K. A. |
| Title: | Parameter sensitivities, Monte Carlo filtering, and model forecasting under uncertainty. |
| Journal: | Journal of Forecasting
1991 : JAN, VOL. 10:1-2, p. 117-133 |
| Index terms: | PREDICTION THEORY MONTE CARLO TECHNIQUE ENVIRONMENT UNCERTAINTY |
| Language: | eng |
| Abstract: | Estimation of the consequences of today's environmental problems requires the prediction of effects which cannot always be directly observed. Complex models are used to make predictions over a broad range of temporal and spatial scale. Data for adequate estimation are, however, often not available. Monte Carlo filtering, the process of rejecting sets of model simulations that fail to meet specified criteria of model performance, is a useful procedure for establishing parameter values and improving model confidence. A foodweb model is used to examine the relationship between model sensitivities and Monte Carlo filtering results, producing substantial reductions in model forecasting uncertainties. |
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