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Three of the current six candidate experiments involve international collaborations.
The number of components in the PCA model also gives the rank of the parameter space induced by the candidate experiments.
The second step is to define a full combinatorial library of all selected items and to construct a matrix, D, of candidate experiments in which the selected items are represented by their principal property scores.
After performing sensitivity analysis of many candidate experiments, a latent variable model (PCA) is made from the resulting sensitivity matrix and the score matrix is used as a candidate set prior to experiment selection.
In this setting, the number of candidate experiments amounts to.
Therefore, the observables of candidate experiments are related to our prediction of interest.
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This is useful information when fitting many parameters in a microkinetic model and provides an assessment of the value of every candidate experiment before it is even performed.
We simulate a PPD for each candidate experiment.
At the heart of this strategy is the evaluation of the entropy of a candidate experiment e according to the predicted response of different models.
The expected risk R e; π) of a candidate experiment e given our current estimate of the parameter distribution π is the criterion we propose in order to assess the relevance of performing e.
The calculation of the A-optimality of a candidate experiment with a 12×12 FIM (simple finite differences) requires 48 model simulations; the Bayesian concept required 1000 simulations per prediction, rendering it 20 times more simulation intensive than the Fisher design in this example.
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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