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| Titel |
Technical Note: Approximate Bayesian parameterization of a process-based tropical forest model |
| VerfasserIn |
F. Hartig, C. Dislich, T. Wiegand, A. Huth |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 11, no. 4 ; Nr. 11, no. 4 (2014-02-27), S.1261-1272 |
| Datensatznummer |
250117252
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| Publikation (Nr.) |
 copernicus.org/bg-11-1261-2014.pdf |
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| Zusammenfassung |
Inverse parameter estimation of process-based models is a long-standing
problem in many scientific disciplines. A key question for inverse parameter
estimation is how to define the metric that quantifies how well model
predictions fit to the data. This metric can be expressed by general cost or
objective functions, but statistical inversion methods require a particular
metric, the probability of observing the data given the model parameters,
known as the likelihood.
For technical and computational reasons, likelihoods for process-based
stochastic models are usually based on general assumptions about variability
in the observed data, and not on the stochasticity generated by the model.
Only in recent years have new methods become available that allow the
generation of likelihoods directly from stochastic simulations. Previous
applications of these approximate Bayesian methods have concentrated on
relatively simple models. Here, we report on the application of a
simulation-based likelihood approximation for FORMIND, a parameter-rich
individual-based model of tropical forest dynamics.
We show that approximate Bayesian inference, based on a parametric likelihood
approximation placed in a conventional Markov chain Monte Carlo (MCMC)
sampler, performs well in retrieving known parameter values from virtual
inventory data generated by the forest model. We analyze the results of the
parameter estimation, examine its sensitivity to the choice and aggregation
of model outputs and observed data (summary statistics), and demonstrate the
application of this method by fitting the FORMIND model to field data from an
Ecuadorian tropical forest. Finally, we discuss how this approach differs
from approximate Bayesian computation (ABC), another method commonly used to
generate simulation-based likelihood approximations.
Our results demonstrate that simulation-based inference, which offers
considerable conceptual advantages over more traditional methods for inverse
parameter estimation, can be successfully applied to process-based models of
high complexity. The methodology is particularly suitable for heterogeneous
and complex data structures and can easily be adjusted to other model types,
including most stochastic population and individual-based models. Our study
therefore provides a blueprint for a fairly general approach to parameter
estimation of stochastic process-based models. |
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