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Functionally Induced Priors for the Analysis of Experiments

Summary: [This abstract is based on the authors' abstract.]The concept of using functional priors for the design and analysis of three-level and higher-level experiments is considered. When developing prior distribution for model parameters, a factor may be qualitative or quantitative, therefore, appropriate correlation functions and coding schemes are proposed so that the prior distribution is simple and the results are interpretable. The prior incorporates principles that help resolve the aliasing problems in fractional designs. The method is demonstrated through the analysis of real experiments.

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  • Topics: Design of Experiments
  • Keywords: Alias,Bayesian methods,Design of experiments (DOE),Gaussian curve,Probability function
  • Author: Joseph, V. Roshan; Delaney, James Dillon
  • Journal: Technometrics