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The approach derived in the previous section can be easily generalized for arbitrary given continuous interpolating functions.
The construction method can be easily generalized for any number of periods and subjects, as long as they are both a multiple of the number of treatments.
The above result can be easily generalized for the function of three variables, i.e., for the function f t, u(t), v(t)).
The previous results considering a single carrier can be easily generalized for an OFDM-based system, assuming N c parallel frequency flat fading channels.
CRDS can be easily generalized for use in similar applications that need a rules-based system for selecting the appropriate item for a given dataset; we give some examples of such generalizations that will likely be used for JWST.
The calculation can be easily generalized for input patterns that push g above and below g 0 multiple times, although they will be non-optimal by our earlier arguments.
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Note that these analyses are easily generalized for a given problem at hand by first estimating the response kernel of a given neuron under a given stimulus, and then simulating data with this response kernel and stimulus, varying parameters of the two models.
Nevertheless, as far as we know there is no systematic study on initializing the free parameters in the lattice structure of ALLPPRFB, and the required method cannot be easily generalized from those for CLLPPRFB.
Moreover, the neural circuit we design can be easily generalized to implement inference for other problems, such as the multi-stimuli cause inference and the same-different judgment.
As before, Censor et al.'s subgradient extragradient method [10, Theorem 3.1] can be easily generalized by using some adaptive step rule, for example, (5.1).
It should be easily generalized to other areas that demonstrate phase precession (for example CA3).
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