Bayesian design and analysis of computer experiments: Two examples. (English) Zbl 0827.62029

Mitchell, Toby J.; Morris, Max D.

Stat. Sin. 2, No. 2, 359-379 (1992).

Summary: In a computer experiment, the data are produced by a computer program that models a physical system. The experiment consists of a set of model runs; the design of the experiment specifies the choice of program inputs for each run. This paper demonstrates two applications of a Bayesian method for the design and analysis of computer experiments to predict model output corresponding to input values for which the model has not been run. When the original code is long-running, the fast predictor produced by this method can serve as an efficient, though approximate, substitute. The models used in the two examples are (i) a computer model for the combustion of methane and (ii) a computer model that simulates the compression molding of sheet molding compound in the manufacture of an automobile hood.

MSC:

62F15	Bayesian inference
65C20	Probabilistic models, generic numerical methods in probability and statistics
62P99	Applications of statistics

Keywords:

Bayesian prediction; interpolation; optimal design; stochastic process; computer experiments; combustion of methane; compression molding