Sentence examples for by numerical maximization of from inspiring English sources

The pulses are designed by numerical maximization of BS sequence sensitivity, subject to constraints that ensure low on-resonance excitation.

The optimal maturation function was computed by numerical maximization of the net reproductive rate, R0, because hatchery capacities limited juvenile releases in early life.

For all fixed-effect codon models described in this paper (Table 2) the tree topology is fixed a priori and, with the exception of the codon frequencies, the parameter values are estimated by numerical maximization of the likelihood function.

Finally, the likelihood is a product over all the L loci (4) Parameters θ0, θ1, τ0, and τ1 are estimated by numerical maximization of the log likelihood ℓ = log { f (D | θ 0, θ 1, τ 0, τ 1 ) }.

Estimates of the causal effects of X1 and X2 on Y (β1 and β2) can be obtained by numerical maximization of this likelihood function or by Bayesian methods (18).

Estimates of the parameters were obtained by numerical maximization of the log-likelihood, defined as, where the first sum is on all observed cases where s i is the sex of individual i, and the second is over both sexes (s) and requires integration of the intensity over the domain (in time and age) D = [0,100] × [1 Jan 1980,31 Nov 2001].

The ML estimate of α was determined by unidimensional numerical maximization of the log-likelihood function [15], conducted using the fminbnd function of Matlab 6.1 over the interval (0.001,1000).

As noted by Royle (2004a), numerical maximization of 2013 requires the replacement of the infinite summation over by a sum with upper limit K.

Numerical maximization of (19) can be performed by using the RS method (Rigby and Stasinopoulos 2005) which is available in the gamlss package (R Development Core Team 2013), SAS (Proc NLMixed) or the Ox program (sub-routine MaxBFGS) (see Doornik 2007) or by solving the nonlinear likelihood equations obtained by differentiating (19).

Second, it is often bimodal, and one of its peaks can be so peculiar that numerical maximization of the extended likelihood frequently fails to locate the global maximum.

We propose a method for maximum likelihood estimation which is an alternative to direct numerical maximization of the likelihood that sometimes exhibits non-convergence problems.