This session is for R&D product and process engineers responsible for innovating quickly to be the first to market with a new product.

Bayesian Optimization is an iterative learning technique that uses a model to generate new candidate runs for assessment. Bayesian Optimization learns from the responses with each iteration and gives us clearer guidance than traditional approaches about when we can stop experimenting. The iterative nature has the potential to dramatically reduce the time and resources required for process and product development, so may be a natural fit for many R&D cultures.

Starting with existing data, either historical or from an initial set of experimental runs, a Gaussian Process model (GaSP) is constructed. Then, using the model predictions, along with the prediction uncertainty and the response goals, new candidate factor combinations are generated to test. After test, the model is updated with the new data. This sequence of steps repeats until the response goals are met and an optimal combination of factor settings is discovered.

New enhancement to JMP Pro incorporate an interactive new platform for Bayesian Optimization.

"This technique is not for determining how well the model fits. Rather, it is a way to discover new regions to explore where we don't have data yet." C. Gotwalt

JMP Chief Data Scientist and a key Developer of the new Bayesian Optimization, Chris Gotwalt, demonstrates and explains the capability and the underlying statistical approaches it deploys. The session includes time for Q&A.

This JMP Developer Tutorial covers: interactively generating the initial Gaussian Process model; specifying new candidate factor combinations to test or run; exploring design space; summarize Gaussian Process model fit; comparing and interactively doing what-if analyses on factors and responses.

Resources

• Active Learning in JMP ENBIS Workshop
• The Structure of Scientific Revolution, Second Edition Enlarged, Thomas S. Kuhn
• A Tutorial on Bayesian Optimization, Peter I. Frazier, July 10, 2018.
• Surrogates: Gaussian process modeling, design and optimization for the applied sciences, by Robert Gramacy
• Taking the Human Out of the Loop: A Review of Bayesian Optimization, Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams and Nando de Freitas