Gaussian Process (GP) is one of several analysis techniques that are used to build approximation models for computer generated experiments. Generally, a space filling design is used to guide the computer experimentation efforts because all the parameters/variables are derived from or directly pulled from first principles physics models/equations. Space filling designs are used because the data generated by the computer experiments is deterministic and likely to be highly non-linear. This is where GP comes into play. Because the data is deterministic, GP will attempt to fit every point in the design perfectly allowing for a close approximation of the true model. We will compare GP to Response Surface and Neural Net models. We will also compare GP models derived from different types of space filling designs.

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Bill Worley는 JMP Global Enablement Team에서 Sr Systems Engineer로 일하고 있습니다. SAS에 합류하기 전에는 Procter & Gamble의 모델링 및 시뮬레이션 그룹의 기술 리더로 JMP 사용법을 교육하고 지원하는 업무를 담당했습니다. 신시내티 대학교에서 화학 석사 학위를 받았습니다.