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@ARTICLE{Sadegh:187136,
      author       = {Sadegh, Mojtaba and Vrugt, Jasper A.},
      title        = {{A}pproximate {B}ayesian {C}omputation using {M}arkov
                      {C}hain {M}onte {C}arlo simulation: {DREAM} ({ABC})},
      journal      = {Water resources research},
      volume       = {50},
      number       = {8},
      issn         = {0043-1397},
      address      = {Washington, DC},
      publisher    = {AGU},
      reportid     = {FZJ-2015-00810},
      pages        = {6767 - 6787},
      year         = {2014},
      abstract     = {The quest for a more powerful method for model evaluation
                      has inspired Vrugt and Sadegh (2013) to introduce
                      “likelihood-free” inference as vehicle for diagnostic
                      model evaluation. This class of methods is also referred to
                      as Approximate Bayesian Computation (ABC) and relaxes the
                      need for a residual-based likelihood function in favor of
                      one or multiple different summary statistics that exhibit
                      superior diagnostic power. Here we propose several
                      methodological improvements over commonly used ABC sampling
                      methods to permit inference of complex system models. Our
                      methodology entitled DREAM(ABC) uses the DiffeRential
                      Evolution Adaptive Metropolis algorithm as its main building
                      block and takes advantage of a continuous fitness function
                      to efficiently explore the behavioral model space. Three
                      case studies demonstrate that DREAM(ABC) is at least an
                      order of magnitude more efficient than commonly used ABC
                      sampling methods for more complex models. DREAM(ABC) is also
                      more amenable to distributed, multi-processor,
                      implementation, a prerequisite to diagnostic inference of
                      CPU-intensive system models.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {246 - Modelling and Monitoring Terrestrial Systems: Methods
                      and Technologies (POF2-246) / 255 - Terrestrial Systems:
                      From Observation to Prediction (POF3-255)},
      pid          = {G:(DE-HGF)POF2-246 / G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000342632300031},
      doi          = {10.1002/2014WR015386},
      url          = {https://juser.fz-juelich.de/record/187136},
}