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Figure 8 Artemisinin data: prediction error estimates for SA-kNN.

Figure 6 Solubility data: prediction error estimates for TS-PCR.

For the solubility data, prediction error estimates from the outer loop agree with those obtained from the 'oracle' data.

In case of the artemisinin data, prediction error estimates from the outer loop again agree with those obtained from the comparatively small 'oracle' data set.

Considering results shown here and those determined with alternative well data, prediction errors were about 2.5 times the fitting errors on average and less than four times the fitting errors overall.

Both generalised Bayesian (active inference) and Kalman filtering predict the current state of the system using prior knowledge (about previous target locations) and refine these predictions using sensory data (prediction errors).

In [10], Thodi and Rodriguez proposed a histogram shifting method for embedding data in prediction errors.

For each value of prediction interval, the prediction is conducted over each of the five sets of data and prediction errors are calculated after each prediction.

error in field data, model prediction errors: NFI to LiDAR and LiDAR to satellite imagery) based on all LiDAR-estimated AGB, satellite imagery, Cubist machine learning algorithm, and Monte Carlo analyses Fig. 12 National forest AGB map based on LiDAR-estimated AGB (with uncertainties < 50%), satellite imagery, Cubist machine learning algorithm, and Monte Carlo analyses.

In order to generate statistically robust results (independent of data and prediction errors) we did not analyze single gene expression patterns but argue on groups of correlated genes.

The precision of sensory data (or prediction errors at any level of the hierarchy) and prior beliefs are not fixed: they can be optimized by attention to best reflect uncertainty about their contribution.