Working directly with the Profiler and improved algorithms for fitting Gaussian process models, Bayesian Optimization has eliminated much of the cumbersome statistical machinery that gets between the experimenter and the solution, while at the same time filling in institutional knowledge gaps.
With as few as two starting experimental runs, Bayesian Optimization can suggest the best next run to maximize the information gained from every experimental dollar. Whether you have historical data or are beginning from scratch, you can learn quickly.
Bayesian Optimization learns from the responses with each iteration and gives the experimenter more precise guidance than traditional approaches on when they can stop experimenting.
In high process and product innovation industries with rapid learning cycles, Bayesian Optimization is impactful, significantly reducing the time and resources needed for experimental design.
