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The core idea behind DE is a scheme for generating trial parameter vectors.
A trial parameter vector with high possibility of having fitness worse than that of the current target vector is called a possibly useless trial vector (PUT vector).
This quantifies the reduction of uncertainty about the trial parameter (X) gained by a single-trial observation of the spike count (Y).
A strategy to choose effective summary statistics is suggested in cases where the stochastic computer model can be run at many trial parameter settings, as in the example.
The set of 19,000 trial parameter vectors generated during DE is quite robust in accounting for most phenotypes (∼110 phenotypes have an acceptance ratio greater than 0.5).
Another variation in the algorithm is the criterion we use for deciding when a trial parameter vector can replace a parent.
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Trial parameters were set as follows.
To optimise the intervention V. To determine trial parameters VI.
Study visits and regular telephone conferences will allow for a 100% check of essential trial parameters.
Two independent reviewers (YL, BQ) will independently extract the key trial parameters using a standardised data abstraction form and assess the risk of bias.
Aspirants and incumbents both said that making the effort to clearly state trial parameters reduced the occurrence of both types of misunderstandings.
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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