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In particular in this case we estimated the delay for a resource retrieval.
We assumed that subjects estimated the delay D = T-t until reward delivery from the number of black patches at each step, albeit with some uncertainty.
From these, we estimated the delay from symptom onset to hospitalisation using a parametric mixture survival model, 18 giving an estimate (assumed unbiased) of the proportion of confirmed cases hospitalised, which we used indirectly in our Bayesian analysis.
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We estimate the delay on our first shipment to be about two weeks (August 11th), although we are hoping for less.
This is done by looking at Table 1 for estimating the delay of operators.
Analytical delay models were proposed for estimating the delay to left-turning vehicles at intersections with the CLL design.
Note that the mobile RS should re-estimate the delay on the edge links in case of handover.
The method utilises the position data of control rod to estimate the delay time in electromagnet clutch release.
Then, it estimates the delay in propagating the data from the body sensor to the body relay.
A direct way to estimate the delay between two receivers is to identify the largest peak in their CCF.
Since the transition probabilities in a Markov chain are sometimes partially unknown, we propose a method of estimating the delay for the next sampling time instant.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com