Video 1: Question on Bayesian Optimization use cases
Victor: Maybe we can share the acquisition functions in the platform and start explaining them?
Yuliana: I am observing that agentic ai propose kind of surrogate DOE + gaussian process as active learning workstream of experimentation. I still have impression that BayesOpt on JMP performs better in case we want to go faster, and I still can not understand why... probably there are more acquisitions functions.
Answer: Depends also on the configuration of the Gaussian Process, and the acquisition functions worked by the JMP team are effective.
Video 2: Possible use case to use Bayesian Optimization
Victor: Don't hesitate to mix approaches: start with small DOE's and continue with BO for example. Might not be appropriate with blocking or blocking with iteration.
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Video 3: Constrained DoE - disallowed combinations, restrictions on responses, covariates
Victor: If you don't have hard to change factors and you have multiple runs per batch, maybe using the "iteration" column as a block when running a regression model
Can you change the factors definition? Instead of having X1 and X2 and a constraint on the ratio, use the ratio and total quantity in your factors?
Yuliana: Is the ratio a linear constraint (system of 2 linear equations)?
Sometimes I use responses that you can get quickly as factors (in place of real factors) to modelize more complex response
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