DATA INSIGHT
ON-DEMAND WEBINAR
Experiment faster and more efficiently with Bayesian optimization
An unpredictable number of experiments, wasted budget and time, and missed optimization targets place significant pressure on R&D groups as they work to move faster, increase capacity, and remain competitive. Bayesian optimization offers a modern approach to experimentation, using prior information - such as historical data, initial experiments, or partial results - to intelligently select the next best experiments to run.
Using Bayesian optimization means fewer trials, more information from each run, and a faster path to true understanding; for scientists and engineers, it means quicker insights, better-informed decisions, and progress with less trial and error.
Watch the recording of our live webinar to learn how Bayesian optimization can help speed experimentation and improve outcomes across your organization.
Key takeaways:
- How Bayesian optimization uses existing data to guide smarter experiment selection.
- How to reduce the number of experimental runs while gaining more insight per test.
- How to achieve optimization more quickly, even with limited time and resources.
About the presenter
Stuart Little
Stuart Little is a Senior Systems Engineer at JMP, where he applies a background in the chemicals industry to helping provide solutions to a broad range of data and statistical problems. He has previously been Lead Research Scientist at Croda, a chemical products manufacturer. Little holds a Ph.D. in chemistry from the University of Sheffield.