Abstract:

In computer experiments, simulation of complex phenomena requires a large number of inputs, and identifying the inputs which make a notable impact on the outputs is of crucial importance. A new Bayesian variable selection algorithm is proposed for computer experiments via a hierarchical sparseness prior. The new algorithm is not only capable of deleting insignificant variables and estimating coefficients of significant variables simultaneously, but also has no necessity to adjust the sparseness-controlling hyperparameters. Numerical implementation is carried out by a kind of fast algorithm and experimental results show that the new approach not only yields more accurate variable selection but also is of low computational complexity.