Gal, Y. & Ghahramani, Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning.
Physical parameter sets in the resulting Pareto front approximation represent best trade-offs with respect to the multiple objectives.
The spatial series of SOC and environmental variables along each of three transects were separated into six detail components and one approximation representing different scales using wavelet decomposition.
This approximation represents a good approach for any singular integral given on the curve in the sense of Cauchy principal value.
In fact, the proof of the convergence of the Carathéodory approximation represents an alternative to the procedure for establishing the existence and uniqueness of the solution to delay DPSDEs.
One of the thermodynamic methodologies to assess the distillation column operation is the column targeting tool (CTT), which is based on the practical near-minimum thermodynamic condition (PNMTC) approximation representing a practical and close to reversible operation [7, 8].
The second approximation represents the time needed for deterministic loss by mutation accumulation.
The first approximation represents the waiting time until the first mutant destined for loss, with loss then occurring relatively rapidly by genetic drift, taking time Ne<<1/m.
However, this, and other, encapsulating approximations, like all approximations, represent a trade-off of detailed representation versus conceptual clarity.
