Sentence examples for update the estimation of from inspiring English sources

Exact(5)

Therefore, the phase error correction and Doppler shift estimation are done in a mutually iterative manner, in which one estimated variable is used to update the estimation of the other.

Filters refer to a class of data assimilation algorithms designed to update the estimation of the state in an on-line fashion, as data is acquired sequentially.

We first update the estimation of the number of orphan enzymes and interpret their decrease in the light of past and recent enzyme activity discoveries.

To update the estimation of the adult HIV prevalence and number of people living with HIV (PLHIV) in India for the year 2008−2009 with the combination of improved data and methods.

Estimation-step: To update the estimation of the density f (t) of current iteration t to f (t+1) for the next iteration t + 1, we first compute candidate sub-marginal density f̃ jl(· |B̃ s) for individual l in pedigree j at locus k and cluster s.

Similar(55)

Use of a shallow reference population, most closely related to the selection candidates, gave early benefits while in later generations, when marker effects were not updated, the estimation of marker effects based on a deeper reference population did not pay off.

The porosity estimate was updated during the estimation of mass transfer rate parameters to account for the effect of pore diffusion on bulk flow.

Several aspects on the possible usage of mobile phone records and social media status updates in the estimation of official data, such as census, demographic or land use records have been discussed in recent papers.

This approach gave early benefits but in later generations, without updating marker effects, the estimation of marker effects based on less related reference individuals did not pay off.

These soft-decision decoding algorithms iteratively update the likelihood probability estimation of each bit.

Similar results have been shown in [17] for classical CSP; the suggested bias update requires only the estimation of one single parameter and is therefore more robust.

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