Bayesian Optimization or Traditional Design of Experiments? Finding the Best Path to Insight
Not all experimental challenges call for the same approach. Some benefit from broad exploration, others from careful refinement, and some from a mix of both. In this hands‑on workshop, participants learn how to choose experimental strategies that fit their goals, constraints, and stage of learning. Using practical examples, we explore how considerations like learning versus optimization, resource limitations, and opportunities for sequential experimentation influence strategy selection. Along the way, we also highlight how traditional design of experiments and Bayesian optimization complement one another, and how these methods can be used together to efficiently solve real‑world experimental problems.
Workshops are included in the price of your conference registration. Space is limited, so these sessions must be selected during your conference registration to confirm your participation.
Please note: This workshop uses traditional DOE tools available in JMP, as well as Bayesian Optimization in JMP Pro. We can provide access to a virtual lab to use on your laptop if you want to try this feature but don’t have JMP Pro installed at the time of the workshop.
