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White area: Represents the learning period, where posterior models are produced as a result of updating the prior model, by using the RGI data.
For each case this paper has chosen the posterior models that are obtained after four days of updating the prior model.
(6) White area: Represents the learning period, where posterior models are produced as a result of updating the prior model, by using the RGI data.
The traditional workflow is to calibrate prior models to match observed data, and then use the posterior models to simulate future system performance.
We discuss to which degree the posterior BMA mean outperformed the prior BMA mean and all individual posterior models, how informative the data types were for reducing prediction uncertainty of actual evapotranspiration, and how well the model structure can be identified based on the different data types and subsets.
This improves heuristic search procedures as it restricts the space of the possible posterior models (see Methods).
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Analytical Posterior Model Probabilities are displayed in the red line.
Fig. 2 Posterior Model Size Distribution and Posterior Model Probabilities: model size reflects the number of variables suggested by prior and posterior distribution of potential variables (mean 7.003).
Based on this resulting posterior model, forward simulator is used to generate predicted posterior model values for the future mining operations (for next two days).
In Fig. 2, model size distribution and the posterior model probabilities are shown.
These are derived from the COV-OBS.x1 field model and its posterior model error covariance matrix.
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