X. J. Tian, Z. H. Xie, and A. H. Wang, A new approach for Bayesian model averaging, Science China Earth Sciences, vol.115, issue.8, pp.10-10007, 2011.
DOI : 10.1007/s11430-011-4307-x

A. Ciccone and M. Jaroci´nskijaroci´nski, Determinants of Economic Growth: Will Data Tell? American Economic Journal: Macroeconomics, forthcoming, 2010.
DOI : 10.1257/mac.2.4.222

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.7501

J. Vrugt, C. Ter-braak, C. Diks, B. Robinson, J. Hyman et al., Accelerating Markov chainMonteCarlosimulationbyself-adaptivedifferentialevolutionwithrandomizedsubspacesampling, Int J Nonlinear Sci Numer Simul, 2008.

J. Vrugt, C. Ter-braak, M. Clark, J. Hyman, and B. Robinson, Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation, Water Resources Research, vol.33, issue.3, pp.10-1029, 2008.
DOI : 10.1029/2007WR006720

T. Wöhling and J. Vrugt, Combining multi-objective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models, Water Resour Res, vol.doi, pp.10-1029, 2008.

N. Ajami, Q. Duan, and S. Sorooshian, An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resources Research, vol.204, issue.6, pp.10-1029, 2007.
DOI : 10.1029/2005WR004745

A. E. Raftery, T. Gneiting, and F. Balabdaoui, Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Monthly Weather Review, vol.133, issue.5
DOI : 10.1175/MWR2906.1

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.9558

N. K. Ajami, Q. Duan, X. Gao, and S. , Sorooshian Multi-model combination techniques for hydrological forecasting: application to distributed model intercomparison project results, J Hydrometeorol, vol.8, pp.755-768, 2006.
DOI : 10.1175/jhm519.1

R. Rajagopal, &. Enrique, and D. Castillo, Model-Robust Process Optimization Using Bayesian Model Averaging, Technometrics, vol.47, issue.2, pp.152-163, 2005.
DOI : 10.1198/004017005000000120

H. Xie, J. W. Eheart, and Y. Chen, An approach for improving the sampling efficiency in the Bayesian calibration of computationally expensive simulation models A dual-pass variational data assimilation framework for estimating soil moisture profiles from AMSR-E microwave brightness temperature, Water Resour. Res. J. Geophys. Res, vol.45, pp.10-102910, 1029.

X. J. Tian, Z. H. Xie, and A. G. Dai, A microwave land data assimilation system: Scheme and preliminary evaluation over China, Journal of Geophysical Research, vol.58, issue.D17, pp.10-1029, 2010.
DOI : 10.1029/2010JD014370