Experimenters' Club (prior DOE Club)

Wishing all our members a joyful holiday season and a fantastic start to 2026! :party_popper: We look forward to connecting with you through more meetings and learning opportunities in the coming year: 27 Jan. - Q1 Experimenters' Club 28 Apr. - Q2 Experimenters' Club Thank you for being part of our community—here’s to learning, sharing, and growing together in 2026! ... View more

This Club is a participant-driven meeting designed to encourage attendee involvement and knowledge sharing. Join us for these lightly structured discussions to share your ideas and learn more about Design of Experiments (DOE), Bayesian Optimization, and other advanced optimization strategies in JMP. Whether you’re exploring experimental design or looking to optimize complex processes, this is the place to connect, ask questions, and learn together. What can you expect? Quarterly meetings focused on experimentation and optimization. Lightly structured discussions about DOE, Bayesian Optimization, and related techniques. Experts and novices are welcome—bring your questions, share your experiences, and learn from the community. Learning and networking opportunities to deepen your understanding of data-driven design and optimization. Please send in your questions in advance to help us get the ball rolling. Register here       ... View more

This Club is a participant-driven meeting designed to encourage attendee involvement and knowledge sharing. Join us for these lightly structured discussions to share your ideas and learn more about Design of Experiments (DOE), Bayesian Optimization, and other advanced optimization strategies in JMP. Whether you’re exploring experimental design or looking to optimize complex processes, this is the place to connect, ask questions, and learn together. What can you expect? Quarterly meetings focused on experimentation and optimization. Lightly structured discussions about DOE, Bayesian Optimization, and related techniques. Experts and novices are welcome—bring your questions, share your experiences, and learn from the community. Learning and networking opportunities to deepen your understanding of data-driven design and optimization. Please send in your questions in advance to help us get the ball rolling. Register here ... View more

Introduction to JMP 19 new features with focus on Bayesian Optimization Further information: https://www.jmp.com/en/software/new-release/new-in-jmp Question 1: If we don't have historical data set, can we start a Bayesian Optimization? Victor: Yes, you can start with Space-Filling runs to cover your experimental space  Jonas : It needs at least one row in a data set as well as some initial column information. It will add more runs to explore more of the design space in case you start with only one or a few rows. Yes, basically the platform needs some metadata and column informations to be able to run (design role and coding at least)  Victor: I would start with a simple basic design first Maria: Developer Tutorial on Bayesian Optimisation: https://community.jmp.com/t5/Learn-JMP-Events/Developer-Tutorial-Bayesian-Optimization/ev-p/894910?… Victor: You can try Scoping Designs | Prism to check the variability of your response and its variability regarding the factors Winfried: It is i the name: this is particularly useful, if you believe you can start soon to optimise. If you are still early in the process of understanding your problem, use other design tools, like screening factors or space filling designs! Victor: Scoping Design DoE JMP Add-In - JMP User Community Winfried: Be aware of time trend with any sequential method like BO! Remember, why we are generally use randomisation!  Victor: Also important to emphasize that DoE is not a static, one-design-answers-all-questions methodology; like Bayesian Optimization, you can progress by iterations, by augmenting your design step by step : start with a screening design, augment with higher order effects, then augment with space-filling ... The possibilities are immense, and you can combine these steps with Bayesian Optimization  Winfried: Be aware of time trend with any sequential method like BO! Remember, why we are generally use randomisation! ;) Yes, no protection in BO like Blocking or other time-trend robust designs that we can find with DoEs !  Victor: overview of optimization methods : The Evolution of Optimization Methods | by Victor Guiller | Medium Question 2: Is there a tool like prediction profiler which you could use for design spaces with many factors? Victor: What is the practical difficulty behind using Prediction Profiler for 6+ factors? Optimize? You might reduce your factors space based on the importance of factors on your response(s) Anthony: could reduce based on effect size as well. the practical significance question. if some are very low compared to the others you could reduce on those at first. Victor: PCA might not work well using independent factors Anthony: PCA I’ve used for reducing dimensionality in Y's. i haven't used it for multiple x's. Victor: You can also use the Simulator, create a new dataset with predictions, and create your own visualizations.   Question 3: Including different start material product quality in the DOE designs. You can use random blocks to allocate different batch/product qualities across your DOE runs? Jonas: Covariate factors maybe? Anthony: treat as a factor, covariate, blocking all come to mind as options Manlio: you could use blocking for different materials Victor: It depends if you want to assess the impact of batch on mean response vs. variance response (fixed vs. random effects in the model), how representative these batch qualities are, and your objectives    Question 4:  How does Bayesian Optimisation handle iteration of factor addition and removal as the learning journey goes on? Design and noise factors change from experiment to experiment in my current workflow. I'm curious how BayesOpt would approach this.   Further resources:  Developer Tutorial: Bayesian Optimization: https://community.jmp.com/t5/Learn-JMP-Events/Developer-Tutorial-Bayesian-Optimization/ec-p/894910#M833 Topic suggestions for 2026: https://community.jmp.com/t5/Design-of-Experiments-Topic/idb-p/doe-grouphubidea-board Subscribe to get the new sessions in 2026: www.jmp.com/newswire ... View more