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To enable a fair comparison of the best LMM and non-parametric imputations (presented below), the prediction error of the best LMM was estimated by using leave-one-cluster-out cross validation (LOCOCV) [70].
To enable a fair comparison of the best LMM and non-parametric imputations (presented below), the prediction error of the best LMM was estimated by using leave-one-cluster-out cross validation (LOCOCV) [ 70].
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Second order predictions are of the precision (inverse variance) of sensory input, and they optimise the post-synaptic gain of the prediction error units below.
These constitute bottom-up and lateral messages that drive conditional expectations towards a better prediction to reduce the prediction error in the level below.
These constitute bottom up and lateral messages that drive posterior expectations towards a better prediction to reduce the prediction error in the level below.
The prediction error was <10%.
The prediction error interval is then ((0,p_s)).
E F indicates the prediction error.
The prediction error is less than 10%.
a The prediction error percents (PE%) were computed for each concentration value using the equation below and the absolute prediction error percents (|PE|%) was computed as the absolute value of the PE%.
The prediction error (RMS error of prediction, Table 2) ranged between 1.75 and 3.5 times the modeled water-level fitting error (RMS error of fit, Table 2).
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