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Incremental identification is applied to efficiently construct the unknown models from data.
Another criterion of interest is the ability of a learning algorithm k to recover the vector of regression coefficients β i when it is trained on a separate dateset j ≠ i and the unknown models underlying datasets i and j might differ from each other.
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In the algorithm, Γ distribution is used as the prior distribution of the unknown model parameters.
The unknown model coefficients for convection parameter and diffusivity are calibrated from the observational data.
The unknown model constants are tuned to fit to experimental data.
The unknown model of the manipulator is constructed by RBF neural network.
In this verification, a sub-seafloor model consists of two layers, and the unknown model parameters are (ρ1,h1, and ρ2).
The unknown model parameters were the amplitude of the basis functions corresponding to each time window at each subfault.
The unknown model parameters are defined by hyper parameters, and the hierarchical Bayesian method is adopted to estimate parameters.
It is assumed that the unknown model parameters ( {sigma}_{co}^2 ), ( {sigma}_{bl}^2 ), and ( {sigma}_n^2 ) are independent.
The prior models of the noisy projection, the unknown sparse coefficients, and the point spread function are related to the unknown model parameters.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com