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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 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.
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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.
The approach was based on approximating the posterior process with a factorised process where the effect of the interactions between species were replaced by a mean-field approximation.
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.
This approach is based on approximate transformation of chance constraints into deterministic ones.
The low order artificial diffusion is based on approximate Riemann solvers for hyperbolic conservation laws.
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