Uncertainty quantification using Bayesian Evidential Learning (BEL) in applied geophysics : pyBEL1D for fast and accurate posterior model spaces

Michel, Hadrien

Uncertainty quantification using Bayesian Evidential Learning (BEL) in applied geophysics : pyBEL1D for fast and accurate posterior model spaces

Hadrien Michel, F.R.S.-FNRS (Fonds de la Recherche Scientifique), Brussels, Belgium, Frederic Nguyen, Université de Liège, Urban and Environmental Engineering, Liege, Belgium and Thomas Hermans, Ghent University, Department of Geology, Gent, Belgium