Generative models for DoE size - makes sense?

Modeling
1

Hi,

I want to make a fake example to present how

  1. the experimental error and
  2. the measurement error

can affect models built from lab experiments using DoE. However, I am not sure that the approach is correct. As it’s the first time using simulated results I don’t feel confident that what I’m doing makes sense.

  1. For this reason I made a fake model.
  2. A fake DoE
  3. And I get fake responses.
  4. Then, I randomly take a small sample and I run a model to see if I can recover the true parameters
  5. Finally, I repeat the step 4 multiple times

Now, the DoE is not always the same as I test different size of DoEs e.g. 22 vs 16 runs (but, all of them have the same number of factors and the same min & max levels, per factor)

Do you think that this approach makes sense?
If you have any comments or any related literature please, let me know.

Thanks

2

Hi, sorry we took quite long to respond.

In fact there is recent preprint on Bayesian workflow that deals with exactly those question. If I understand you correctly, what you are doing roughly matches the approach we advocate for in the preprint and is at least a very good start.

Best of luck with your model!

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