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The novelty of the approach consists in adaptation technique for optimization of the system with unknown distribution of the cost function.
To this end, deterministic models were coupled to an adaptive Metropolis-Hastings algorithm to estimate the unknown distribution of the parameters representing the most important effects, namely land leveling, tillage, and crop cover.
The main difficulty of these contrast function based approaches lies in estimating the unknown distribution of the sources, which usually requires a large number of observations [10].
The unknown distribution of the phase noise, needed for this estimation, is obtained in a probabilistic way by applying Monte Carlo methods.
We propose a matrix joint diagonalization approach to solve the complex IVA problem which does not rely on a pre-whitening step nor on the estimation of the unknown distribution of the sources.
We set different thresholds in each channel to label the T-F units based on the response energy feature due to the unknown distribution of the intrusions while the tandem system uses constant threshold.
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Thus, unknown distributions of the underlying Hg data, together with unequal sample sizes for the comparisons of interest, made statistical comparisons inappropriate for our study.
The inverse problem of FMT is to solve an ill-posed linear equation of which the weight matrix maps unknown distributions of the fluorescent markers inside small animals onto the fluorescent measurements over the surface.
Owing to the unknown distributions of the mean differences, non-parametric bootstrapping, a numerical resampling method, was performed (5000 iterations) to obtain reliable estimates of the s.e. for the clinical end points (Fine and Gelber, 2001).
Finally, the following matrix equation of the inverse problem is formed by assembling the sub matrix equations of all the projections as follows (6) ϕ = W X, where W = { W s} is the weight matrix mapping unknown distributions of the fluorescent markers inside the small animal onto the measured fluorescent data over the surface.
This is mostly due to the presence of the protein-bound NADH (Bücher et al. 1972) and unknown distributions of these cofactors over the cytosolic and mitochondrial compartment.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com