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Bayesian Analysis of Adaptive One-Factor-at-a-Time Experimentation

[+] Author Affiliations
Hungjen Wang, Daniel D. Frey, Gordon M. Kaufman

Massachusetts Institute of Technology, Cambridge, MA

Paper No. DETC2007-34926, pp. 285-295; 11 pages
doi:10.1115/DETC2007-34926
From:
  • ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 6: 33rd Design Automation Conference, Parts A and B
  • Las Vegas, Nevada, USA, September 4–7, 2007
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4807-8 | eISBN: 0-7918-3806-4
  • Copyright © 2007 by ASME

abstract

This paper considers the problem of achieving improvements through adaptive experimentation. To limit the focus we consider only design spaces with discrete two-level factors. We prove that, in a Bayesian framework, one factor at a time experimentation is an optimally efficient response to step by step accrual of sample information. We derive Bayesian predictive distributions for experimentation outcomes given natural conjugate priors. Using an example based on fatigue life of weld repaired castings, we show how to use our results.

Copyright © 2007 by ASME

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