In principle, prediction-mapped experimental data could be used as a proxy for predicting sensitivity or accuracy and as a metric for ranking designs.
If we let α to be negative, that reverses the model plot in Figure 2. In this scenario, our observation is in agreement with that of first principle prediction of Yildirim [28].
It discerns three main types of gap: (1) between first-principle predictions and measurements, (2) between machine learning and measurements, and (3) between predictions and display certificates in legislation.
We give quantitative first-principles predictions of: (i) bulk energetics of the Al Cu solid solution and θ′ precipitate phases, (ii) interfacial energies of the coherent and semi-coherent θ′/Al interfaces, and (iii) stress-free misfit strains and coherency strain energies of the θ′/Al system.
Prediction Markets are based on the principle that predictions about any future event can be traded like a stock or option on a virtual market.
In principle, these predictions could also be made using Reaction Difference Matching (RDM) [23], machine learning methods [31, 32], or other rule-based methods such as ChemAxon's Metabolizer.
In principle, interface predictions can be used to remove this limitation and there have been previous attempts in this direction [3], [11].
In principle, "these predictions can anticipate delays if they are part of the generative model," (Friston 2011); however, this anticipation has never been demonstrated formally.
The principles and predictions of other model types are also discussed.
In principle, the prediction accuracy could reflect the average performance of different genes selected in successive iterations.
