Abstract
In many situations, simulation of complex phenomena requires a large number of inputs and is computationally expensive. Identifying the inputs that most impact the system so that these factors can be further investigated can be a critical step in the scientific endeavor. In computer experiments, it is common to use a Gaussian spatial process to model the output of the simulator. In this article we introduce a new, simple method for identifying active factors in computer screening experiments. The approach is Bayesian and only requires the generation of a new inert variable in the analysis; however, in the spirit of frequentist hypothesis testing, the posterior distribution of the inert factor is used as a reference distribution against which the importance of the experimental factors can be assessed. The methodology is demonstrated on an application in material science, a computer experiment from the literature, and simulated examples.
Original language | English (US) |
---|---|
Pages | 478-490 |
Number of pages | 13 |
Volume | 48 |
No | 4 |
Specialist publication | Technometrics |
DOIs | |
State | Published - Nov 2006 |
Keywords
- Computer simulation
- Latin hypercube
- Random field
- Screening
- Spatial process
ASJC Scopus subject areas
- Statistics and Probability
- Modeling and Simulation
- Applied Mathematics