BayesLands: A Bayesian inference approach for parameter uncertainty quantification in Badlands

  title={BayesLands: A Bayesian inference approach for parameter uncertainty quantification in Badlands},
  author={Rohitash Chandra and Danial Azam and R. M{\"u}ller and T. Salles and Sally Cripps},
  journal={Comput. Geosci.},
  • Rohitash Chandra, Danial Azam, +2 authors Sally Cripps
  • Published 2019
  • Computer Science, Physics, Geology
  • Comput. Geosci.
  • Abstract Bayesian inference provides a rigorous methodology for estimation and uncertainty quantification of parameters in geophysical forward models. Badlands (basin and landscape dynamics model) is a landscape evolution model that simulates topography development at various space and time scales. Badlands consists of a number of geophysical parameters that needs estimation with appropriate uncertainty quantification; given the observed present-day ground truth such as surface topography and… CONTINUE READING
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