Bayesian inference is implemented to obtain a polynomial chaos expansion for
the response of a complex model, given a sparse set of training runs. For
polynomial basis reduction, Bayesian compressive sensing is
employed to detect basis terms with strong impact on model output
via relevance vector machine. Furthermore, a recursive algorithm is
proposed to determine the optimal set of basis terms. The
methodology is applied to global sensitivity studies of the Community
Land Model.