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A Bayesian analysis of unreplicated two-level factorials using effects sparsity, hierarchy, and heredity
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.ORCID iD: 0000-0003-3911-8009
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences.ORCID iD: 0000-0003-1473-3670
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
2011 (English)In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 23, no 2, p. 152-166Article in journal (Refereed) Published
Abstract [en]

This article proposes a Bayesian procedure to calculate posterior probabilities of active effects for unreplicated two-level factorials. The results from a literature survey are used to specify individual prior probabilities for the activity of effects and the posterior probabilities are then calculated in a three-step procedure where the principles of effects sparsity, hierarchy, and heredity are successively considered. We illustrate our approach by reanalyzing experiments found in the literature.

Place, publisher, year, edition, pages
2011. Vol. 23, no 2, p. 152-166
Keywords [en]
Bayesian analysis, engineering judgments, Markov chain Monte Carlo integration, posterior probability of active effects, prior information, unreplicated factorials
National Category
Reliability and Maintenance Signal Processing
Research subject
Quality Technology & Management; Signal Processing
Identifiers
URN: urn:nbn:se:ltu:diva-2643DOI: 10.1080/08982112.2011.553760ISI: 000299335400005Scopus ID: 2-s2.0-79952670218Local ID: 04b78670-fc6f-11df-8b95-000ea68e967bOAI: oai:DiVA.org:ltu-2643DiVA, id: diva2:975496
Note

Validerad; 2011; 20101130 (eri_van)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2022-08-23Bibliographically approved

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Bergquist, BjarneVanhatalo, ErikLundberg Nordenvaad, Magnus

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