Design of mixture experiments using bayesian D-optimality

Jose Andere-Rendon, Douglas Montgomery, Dwayne A. Rollier

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


A Bayesian modification is used to reduce the dependency of D-optimal designs on the assumed model. We study the performance of these Bayesian D-optimal designs with respect to the total squared error of prediction and the distribution of information throughout the factor space. The study investigates three and four component, constrained and unconstrained mixture experiments. Some of the designs evaluated perform extremely well with respect to these characteristics. Compared to standard D-optimal designs they produce significantly smaller bias errors, allow the fitting of a larger number of higher order terms, improve the coverage of the factor space, and still have very good variance properties. Practical recommendations are provided for the practitioner.

Original languageEnglish (US)
Pages (from-to)451-463
Number of pages13
JournalJournal of Quality Technology
Issue number4
StatePublished - Oct 1997


  • Bayesian Methods
  • D-Optimality
  • Mixture Experiments

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Design of mixture experiments using bayesian D-optimality'. Together they form a unique fingerprint.

Cite this