Video using JMP 19 was posted in October 2025.

Bayesian Optimization accelerates industrial R&D and lowers the barrier to entry. It saves time and resources by learning from observed response values while using the project goals to recommend the settings to test next.

See how to use JMP Pro Bayesian optimization tools to go beyond the limitations of  traditional DOE, where augmentation is based on previous positioning of experimental points and design space optimality criteria only.

Several case studies show how to:

• Start with some data.
• Define goal(s).
• Obtain model.
• Perform a search to find next experimental data point(s).
• Generate response for new data, update model, and repeat until satisfied.

Resources

• Prediction of compression strength of high performance concrete using artificial neural networks
• Developing an understanding of sophorolipid synthesis through application of a central composite des...
• A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Expe...