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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.
In the literature, one can find some results in IGA over an irregular mesh based on approximating schemes, while little work is reported so far using interpolatory schemes.
Sutton, R. Integrated architectures for learning, planning, and reacting based on approximating dynamic programming.
An approximate analytical solution is obtained, based on approximating the contact area by an ellipse.
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.
To stabilise the solution we employ an engineering approach based on approximating the function f by a polynomial function of temperature.
Similar(32)
Initial approximations based on approximate methods are refined using much higher levels of theory.
This approach is based on approximate transformation of chance constraints into deterministic ones.
Both are based on approximate graph subsumption.
In this work, we propose a new algorithm based on Approximate Bayesian Computation (ABC), to retrieve AOD.
The existing methods are based on approximate analytical models applied in an unrealistically simplified geometry.
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