Abstract
The work of Currin et al. and others in developing fast predictive approximations'' of computer models is extended for the case in which derivatives of the output variable of interest with respect to input variables are available. In addition to describing the calculations required for the Bayesian analysis, the issue of experimental design is also discussed, and an algorithm is described for constructing maximin distance'' designs. An example is given based on a demonstration model of eight inputs and one output, in which predictions based on a maximin design, a Latin hypercube design, and two compromise'' designs are evaluated and compared. 12 refs., 2 figs., 6 tabs.
Keywords
MinimaxLatin hypercube samplingBayesian probabilityVariable (mathematics)HypercubeComputer scienceComputer experimentAlgorithmSensitivity (control systems)Mathematical optimizationApplied mathematicsMathematicsArtificial intelligenceSimulationEngineeringMonte Carlo methodStatisticsParallel computing
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Publication Info
- Year
- 1991
- Type
- report
- Citations
- 288
- Access
- Closed
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Cite This
Max D. Morris,
Toby J. Mitchell,
Donald Ylvisaker
(1991).
Bayesian design and analysis of computer experiments: Use of derivatives in surface prediction.
.
https://doi.org/10.2172/5836957
Identifiers
- DOI
- 10.2172/5836957