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On the technology side, aggregating the large volume of CAT data will be challenging because the data must reflect point-in-time market pricing of the security being traded and be linked to each specific trade event.
In addition, some nodes die fast like stationary nodes because the data must transfer through it which leads to the consumption of its energy.
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It is still computationally costly, because multiple parameter sets that match the data must first be identified [1], [2].
Because https connections are typically slower than regular http connections since the data must be encrypted and decrypted first.
This is because in Bayesian model comparison, the models can be different but the data must be the same.
Research using the data must deal with data quality and validity limitations which arise because the data are not created for research purposes.
If not, the data must remain siloed.
However, this strategy is not compatible with numerical time integration that uses variable time steps in order to meet specified accuracy, because the delayed data must be interpolated compromising the accuracy of the numerical integration.
Detecting events on a second sub-second time scale requires that the volume of measurement data transmitted through the network increase dramatically because the monitoring data must be collected on a second (or sub-second) time scale.
Any aggregation must be safe, so these components work in tandem with the producers because the data sent must be computable by the respective aggregator.
Moreover, in this case whilst estimation of μ1 is still trivial, unbiased estimation of the treatment control comparison does not immediately follow, because the control group data must itself be corrected for some selection bias induced by the stopping rules.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com