STATISTICS SEMINAR SERIES :: A Bayesian Approach to the Design and Analysis of Fractionated Experiments

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Specifying a prior distribution for the large number of parameters in the statistical model is a critical step in a Bayesian approach to the design and analysis of experiments. This article shows that the prior distribution can be induced from a functional prior on the underlying transfer function. The functional prior requires the specification of only a few hyper-parameters and therefore, can be easily implemented in practice. The usefulness of the approach is demonstrated through the analysis of some experiments. The article also proposes a new class of design criteria and establishes their connections with the minimum aberration criterion.


  • Workflow Status: Published
  • Created By: Barbara Christopher
  • Created: 10/08/2010
  • Modified By: Fletcher Moore
  • Modified: 10/07/2016


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