topic Re: Bayesian optimization or similar "one experiment at a time" techniques in Discussions

Bayesian optimization or similar "one experiment at a time" techniques

JaromW — Tue, 19 May 2026 19:03:23 GMT

I'd like to learn more about "Bayesian optimization" or similar "one experiment at a time" techniques available on JMP 18.

Also, will Bayesian optimization be available on the non-Pro JMP platform sometime?

Thanks!

Re: Bayesian optimization or similar "one experiment at a time" techniques

Byron_JMP — Tue, 19 May 2026 21:20:23 GMT

The Bayes Op platform relies on some other features in JMP Pro for the heavy lifting. It's likely to remain a Pro feature for the future.

Depending on what you are trying to do, a small main effects DOE with the minimal number of runs might be a useful way to start. Then, later use Augment Design to add higher order terms for active factors to fill out more of the response surface.

Re: Bayesian optimization or similar "one experiment at a time" techniques

Victor_G — Wed, 20 May 2026 08:03:48 GMT

Hi @JaromW,

Welcome in the Community !

Bayesian Optimization is currently a JMP Pro platform. If you want to learn more about Bayesian Optimization in JMP Pro, I can recommend these ressources:

Bayesian Optimization: A Step-by-step JMP tutorial (Youtube video)

You can also get a quick understanding of Bayesian Optimization with this video: Basics of Bayesian Optimization (Youtube video).

To get the most of Bayesian Optimization (being able to optimize with very few iterations your product/system), a good starting dataset (with high quality information) is needed to get started. Most people start with historical data, but if you have no prior data, I would recommend starting with DoE, either using small screening designs if you have many factors (to filter out important and active factors for the BO iterations), or using small space filling designs (like Latin Hypercube) if you have few factors to get a representative and high quality starting dataset.

DoE and BO are complementary, and as pointed out by @Byron_JMP, there are also many possibilities in the DoE landscape to learn sequentially with small sized designs.

Hope this answer will help you,