Then, substituting (19) and (20a) into (18), the importance weight update is approximated by: w k j ≈ w k − 1 j ∑ s k p ( z k | x ̂ k | k − 1 j, s k, θ ̂ j ) P ( s k | s k − 1 j ) (21).
Model uncertainties are identified using artificial neural networks, and an uncertainty weighting function is approximated for LPV control synthesis.
Taking into account the constraint ∑ i w i = 1, the first weight w 1 is approximated as 1 1 + α + α 2, where α is the common ratio of the sequence.
The patient volume of distribution is approximated by the water equivalent to the patient weight.
The output size is approximated.
The new weights can be approximated with a prior on the relative positions of the facial features as: P i j, k = P i j, k - 1 ∫ p j Y k | X k p j X k | Y 0 : k - 1 d X k ∑ l = 1 M P i l, k - 1 ∫ p l Y k | X k p l X k | Y 0 : k - 1 d X k (16).
However, since Σ depends on the unknown d, the optimal weighting matrix can only be approximated iteratively.
Note that the beamforming weights w t can be approximated by using Taylor series expansion as w i t = w i t 0 + w ′ i t 0 t − t 0 + Ο t − t 0 2 (6).
Making the diffusion constant inversely proportional to molecular weight was based on the assumption that the Stoke's radius of a molecule was approximated by the molecular weight.
Mass weight (Mr) and amino acid (AA) length are approximated values.
