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Analogous to factor models, it is possible to estimate factor loadings that differ across items.
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Given a sequence of observations to be modeled by a VAR model, it is possible to estimate its parameters in closed form by solving a least squares problem.
By fitting simultaneously all obtained autocorrelation functions with the model, it is possible to estimate the diffusion coefficient.
From the obtained model, it was possible to estimate the distribution of pore connectivities of each system.
From the model and measurements, it is possible to estimate the tissue concentrations of Aβ.
By modelling the underlying rate parametrically, it is possible to estimate fitted curves from the model, such as the cumulative survival function.
Markov Chain Monte Carlo sampling is used to estimate the posterior distributions of model parameters and it is shown that it is possible to estimate a tight distribution on the principal axis of diffusion at each voxel using DW-SSFP.
However, it is possible to estimate and model sampling effects using computational methods.
Using this approach, it is possible to estimate the relative precision of several models created from the same data set.
Given commodity flows, it is possible to estimate road freight flows in vehicles by using a vehicle loading model.
Thus, it is possible to estimate the resistance to chloride penetration in concrete incorporating recycled aggregates through the proposed models.
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