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For that reason, our strategy will be based on approximating a maximally-discriminating input using a linearization of the system in (2), and then assessing its suitability for the nonlinear system by comparing the value it achieves to the supremum of the output difference L2-norm over the set of possible inputs.
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In essence, given the data D and a model with parameters θ and latent variables Z, the variational Bayes method is based on approximating the posterior distribution p(Z,θ|D) with a factorial approximation q(Z,θ|ϕ) = q(Z|ϕ Z )q, where ϕ are the variational parameters.
The method is based on approximating the robust GP as a robust linear program (LP), by replacing each nonlinear constraint function with a piecewise linear (PWL) convex approximation.
The approach is based on approximating nonlinearities by means of the first-harmonic Fourier series and carries out a first-harmonic balance of the system dynamics.
The method is based on approximating functions and their derivatives by using the Whittaker cardinal function.
The method is based on approximating a sixth-order mixed derivative by a series of Haar wavelet basis functions.
It is based on approximating the true gradient of the squared error of estimation by its instantaneous estimate.
The circuit proposed in[45] is based on approximating the required function using the first three terms of its Taylor series expansion.
The method, which is based on approximating the junction-tree, improves performance with a reduced number of particles with respect to other NBP algorithms in the literature.
Matrix factorisation (MF) techniques are based on approximating a high dimension matrix A (original data) by a product of two or more lower dimension matrices.
This method is based on approximating the unknown functions by the Chebyshev interpolating polynomials in such a way that they are collocated at the Gauss-Lobatto points defined as follows: (3.8).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com