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However, AIC is related to the expected model output; and for individual data not used in building the predictive model, the expected model is output is obtained with mixed effects set to zero, although nonlinearities may bias expectation - but this is also true for nonlinear models without mixed effects.
This can also be carried out for nonlinear models (2).
This is not the case for nonlinear models.
For nonlinear models, faithfulness can fail and piecemeal perfectibility cannot be guaranteed, raising questions about scientific modeling practices and our understanding of them.
One possible response to the piecemeal confirmation problems discussed here is to turn to a Bayesian framework for confirmation, but similar problems arise here for nonlinear models.
The particle filter is a popular approach to calculate the posterior distribution approximately for nonlinear models [10].
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(b) Mean Squared Error (MSE) versus SNR for nonlinear model parameter estimation produced by artificial signals.
Least squares support vector regression is an advanced artificial intelligence method that can be effectively used for nonlinear modeling.
We formulate and validate our scheme for nonlinear model problems, as well as compressible flow problems.
New methods for nonlinear model reduction of dynamic models, described by nonlinear differential equations have been developed.
For nonlinear model containing a linear substructure, two Rao-Blackwellized particle filtering algorithms have been presented in [131] to yield efficient execution and high accuracy, respectively.
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