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During this time interval, the LSE may have a mismatch between measurement and model, which can create significant solution errors.
This normalization served to emphasize the relative effect of pairing increments with a decrement of light, as well as correct for differences in effective cone strength between measurement and model due to, for example, small movement of the retina in between stimulus presentations.
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Finally, there needs to be active interaction between measurement and modeling.
The mean aims at detecting a possible bias between measurements and model predictions.
The plotted time series reflect the different correlations between measurements and model predictions at different observatories.
Non-linear MFA quantifies fluxes by minimizing the error between measurements and model predictions.
It is also shown that the sequential Bayesian analysis used in the experimental design can also be useful in detecting conflicting information between measurements and model predictions.
In addition, Possibilistic MFA has the ability to detect, and handle, inconsistencies between measurements and model.
A random optimization (Monte Carlo search) of the models yields relative differences between measurements and model systems that are at least three times higher than the difference between the measurements and uncoupled cubic approximation splines.
However, past trends in incidence and data inaccuracies can lead to large discrepancies between measurements and model calculations, and their use requires both caution and expert knowledge [ 47, 62, 63].
The discrepancy between measurements and model is larger for A3 (π = 0.25), but still a band of 10% of deviation around measurements encloses a flux vector compatible with the model.
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