Bayesian design and analysis of computer experiments: Use of derivatives in surface prediction
- Oak Ridge National Lab., TN (USA)
- California Univ., Los Angeles, CA (USA). Dept. of Mathematics
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.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE; USDOE, Washington, DC (USA)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 5836957
- Report Number(s):
- ORNL/TM-11699; ON: DE91013147
- Country of Publication:
- United States
- Language:
- English
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99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
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ALGORITHMS
COMPUTERIZED SIMULATION
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BOREHOLES
MATHEMATICAL MODELS
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OPTIMIZATION
RANDOMNESS
CAVITIES
COMPUTERS
MATHEMATICAL LOGIC
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SIMULATION
990200* - Mathematics & Computers