TY  - JOUR
AU  - Sadegh, Mojtaba
AU  - Vrugt, Jasper A.
TI  - Approximate Bayesian Computation using Markov Chain Monte Carlo simulation: DREAM (ABC)
JO  - Water resources research
VL  - 50
IS  - 8
SN  - 0043-1397
CY  - Washington, DC
PB  - AGU
M1  - FZJ-2015-00810
SP  - 6767 - 6787
PY  - 2014
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000342632300031
DO  - DOI:10.1002/2014WR015386
UR  - https://juser.fz-juelich.de/record/187136
ER  -