Journal Article

FZJ-2015-00810

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Approximate Bayesian Computation using Markov Chain Monte Carlo simulation: DREAM (ABC)

Sadegh, M. (Corresponding Author) ; Vrugt, J. A.FZJ*

2014
AGU Washington, DC

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Please use a persistent id in citations: http://hdl.handle.net/2128/17089  doi:10.1002/2014WR015386

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.

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Contributing Institute(s):

1. Agrosphäre (IBG-3)

Research Program(s):

1. 246 - Modelling and Monitoring Terrestrial Systems: Methods and Technologies (POF2-246) (POF2-246)
2. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)

Appears in the scientific report 2014

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Database coverage:
; ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Engineering, Computing and Technology ; IF < 5 ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

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