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Spatial aggregation of rainfall data from 0.25 degree grid cells to districts was performed by first computing a weight equivalent to estimated proportions of a district comprised by each grid cell.
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We have shown that the problem of error probability estimation is equivalent to estimate the conditional probability density function (pdf) of the observed soft samples at the receiver output.
We begin by arguing that the field estimation problem is equivalent to estimating the sources of the field.
Estimating Equation (2) by OLS is equivalent to estimating Equation (1) by weighted least-squares with inverse variance weights.
The comparison is equivalent to estimate if the input person is younger or older than the other ones.
From (4.11) we know that estimating the error (U-u) is equivalent to estimating the bound of φ, which can be done by using a barrier function technique.
Given the optimization problems (13) and (26), the problem of recovering g through (13) is equivalent to estimating g by (26).
Consequently, the model is "just identified" and the system GMM is equivalent to estimating each equation by two-stage least squares.
Estimating the spectrum-like function p CS Ω = x ^ Ω 2, which is a sort of sparse periodogram, from Equation 6 is equivalent to estimate the spatial energy as a function of the set of assumed spatial frequencies Ω.
Estimating the parameters of the LIF model from this type of data is equivalent to estimating the parameters of a stochastic model from the statistics of the first-passage times only.
Since the SIV g has the same sparsity structure of V, the following theorem shows that the problem of estimating g by optimizing (26) is equivalent to estimating g by using (13).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com