Sentence examples for be obtained by maximization from inspiring English sources
Define 1 = [1, 1] T. With this, the ML estimate X ^ i can be obtained by maximization of the likelihood function p Z i ( d ), Z i ( c ) / X i as: X ^ i = 1 T R i - 1 1 - 1 1 T R i - 1 z i ( c d ).
Simply put, the result of Zanette and Montemurro [ 29] states that any distribution can be obtained by maximization of any q-entropy under the appropriate constraint.
In general, any distribution can be obtained by maximization of the Shannon's entropy under appropriate constraints[ 29], and hence the problem is in the choice of the constraints that are "most natural" for the system of interest.
An approach based on view trees has been adopted in [9] where multiple-view coarse alignment is obtained by maximization of inlier point pairs.
The received signal y(n) is correlated with the PSS of estimated SID on the standard sampling rate, and the refined timing is obtained by maximization of (19).
ML approach for timing and SID in the presence of AWGN is obtained by maximization of the correlation of the received signal with the hypotheses.
The minimum of hydrophilicity and the maximum of tensile strength was obtained by maximization of clay and minimization of plasticizer in the considered range, which resulted in minimum transparency.
The quality of the optimal reconstruction was evaluated by the classical Δ SNR measure and our robust version Δ R. For a comparison, the same images were reconstructed by the filters whose optimal parameters were obtained by maximization of Δ SNR.
Parameters of this model were obtained by maximization of the likelihood of the observed data (see Additional file 11).
Parameter estimates were obtained by maximization of the likelihood with a Newton-Raphson algorithm, and a Likelihood Ratio Test (LRT) was computed at each cM along SSC2.
The prediction profile likelihood (9) PPL (z ) = max θ ∈ { θ | F (D pred, θ ) = z } LL (y | θ ) is obtained by maximization over the model parameters satisfying the constraint that the model response F(D θ∗) after fitting is equals to the considered value z for the prediction.
