@article{fdi:010069521,
  title = {{B}ayesian sparse polynomial chaos expansion for global sensitivity analysis},
  author = {{S}hao, {Q}. and {Y}ounes, {A}nis and {F}ahs, {M}. and {M}ara, {T}. {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{P}olynomial chaos expansions are frequently used by engineers and modellers for uncertainty and sensitivity analyses of computer models. {T}hey allow representing the input/output relations of computer models. {U}sually only a few terms are really relevant in such a representation. {I}t is a challenge to infer the best sparse polynomial chaos expansion of a given model input/output data set. {I}n the present article, sparse polynomial chaos expansions are investigated for global sensitivity analysis of computer model responses. {A} new {B}ayesian approach is proposed to perform this task, based on the {K}ashyap information criterion for model selection. {T}he efficiency of the proposed algorithm is assessed on several benchmarks before applying the algorithm to identify the most relevant inputs of a double-diffusive convection model.},
  keywords = {{G}lobal sensitivity analysis ; {S}obol' indices ; {S}parse polynomial chaos expansion ; {B}ayesian model averaging ; {K}ashyap information criterion ; {D}ouble diffusive convection},
  booktitle = {},
  journal = {{C}omputer {M}ethods in {A}pplied {M}echanics and {E}ngineering},
  volume = {318},
  numero = {},
  pages = {474--496},
  ISSN = {0045-7825},
  year = {2017},
  DOI = {10.1016/j.cma.2017.01.033},
  URL = {https://www.documentation.ird.fr/hor/fdi:010069521},
}