In industrial R&D and process improvement we experiment to understand wickedly complex, multifactor systems. Empirical models of our processes enable us to bring new technologies to market and deliver consistent quality for our customers. Statistical Design and Analysis of Experiments (DOE) is a proven methodology for efficiently capturing the data to build the required model of your process or system. More recently there has been interest in applying "Bayesian Optimization" or "Active Learning" in process and formulation development. These sequential experimentation methods promise greater speed and a simpler workflow for non-statisticians by prioritising goal-seeking over model-building.
JMP, as the leading DOE software and a complete data science tool for scientists and engineers, is the ideal environment for exploring these different approaches. This workshop with JMP Chief Data Scientist, Chris Gotwalt, will be a unique opportunity to see case studies on "model-agnostic" experimentation.
Watch a recording of the workshop from 18 December 2024:
We have also attached the slides, JMP journal and JMP Addin (JMP Pro only) that were used in the workshop.
GaSP-Based Sequential Learning Prototype.jmpaddin
241218 ENBIS Active Learning in JMP Online.jrn
