Choose timezone
Your profile timezone:
ENBIS/JMP short course: Bayesian Desirability Functions – A Modern Approach to Efficient Product and Process Design
→
Europe/Amsterdam
Description
Bayesian Desirability Functions – A Modern Approach to Efficient Product and Process Design
Speakers:
Chris Gotwalt and Phil Kay (JMP)
Date: December 3rd, 3-5 pm CET
Abstract:
In industrial R&D and process improvement we experiment to understand complex, multifactor systems. There has been tremendous interest in applying "Bayesian Optimization" or "Active Learning" to efficiently innovate new products and processes. These approaches to sequential experimentation promise greater speed while being more approachable to scientists and engineers. Generalizing Bayesian Optimization (BayesOpt) to real world complex problems involving multiple responses has proven challenging because in its standard formulation the BayesOpt approach is inherently limited to a single response. In this webinar we review the basics of Gaussian Process regression modeling and the standard approach to BayesOpt. We then introduce the generalization to multiple responses via the Bayesian Desirability framework. We will demonstrate the efficiency and approachability of the technique using new capabilities in JMP Pro 19.
Bio:
Chris Gotwalt leads the statistical software development and testing teams for JMP Statistical Discovery. His passion is developing new technologies that accelerate innovation in industry and science. Since joining the company as a PhD student intern in 2001, Gotwalt has contributed many numerical algorithms and new statistical techniques. He has authored algorithms in JMP for fitting neural networks, linear mixed models, optimal design of experiments, analytical procedures for text analysis, and the algorithms for fitting structural equation models. Gotwalt is a principal investigator for Self-Validating Ensemble Models (SVEM), a procedure that makes machine learning possible for the small data sets often encountered in industry. He holds adjunct professorial positions at North Carolina State University, University of Nebraska and University of New Hampshire, and was the 2020 Chair of the Quality and Productivity Section of the American Statistical Association.
Phil Kay leads JMP's Global Technical Enablement Team and he loves showing people how data analytics enables better science. His passion for this started with some big successes using statistical design of experiments as a development chemist at FujiFilm. Phil has a master’s degree in applied statistics and a master’s and PhD in chemistry. He is a chartered chemist and chair of the Royal Sociey of Chemistry Process Chemistry and Technology Interest group.
Registration
JMP Webinar -- Registration