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Huth, A.; Dislich, C.; Florian, H. &amp; Thorsten, W. (2014): <b>Approximate Bayesian parameterization of a process-based tropical forest model</b>. <i>Biogeosciences</i> <b>11</b>, 1261-1272.

Resource Description

Title: Approximate Bayesian parameterization of a process-based tropical forest model
FOR816dw ID: 1370
Publication Date: 2014-03-01
License and Usage Rights: PAK 823-825 data user agreement. (www.tropicalmountainforest.org/dataagreementp3.do)
Resource Owner(s):
Individual: Andreas Huth
Contact:
Individual: Claudia Dislich
Contact:
Individual: Hartig Florian
Contact:
Individual: Wiegand Thorsten
Contact:
Abstract:
Inverse parameter estimation of process-based<br/> models is a long-standing problem in many scientific disciplines.<br/> A key question for inverse parameter estimation<br/> is how to define the metric that quantifies how well model<br/> predictions fit to the data. This metric can be expressed by<br/> general cost or objective functions, but statistical inversion<br/> methods require a particular metric, the probability of observing<br/> the data given the model parameters, known as the<br/> likelihood.<br/> For technical and computational reasons, likelihoods for<br/> process-based stochastic models are usually based on general<br/> assumptions about variability in the observed data, and not<br/> on the stochasticity generated by the model. Only in recent<br/> years have new methods become available that allow the generation<br/> of likelihoods directly from stochastic simulations.<br/> Previous applications of these approximate Bayesian methods<br/> have concentrated on relatively simple models. Here, we<br/> report on the application of a simulation-based likelihood<br/> approximation for FORMIND, a parameter-rich individualbased<br/> model of tropical forest dynamics.<br/> We show that approximate Bayesian inference, based on<br/> a parametric likelihood approximation placed in a conventional<br/> Markov chain Monte Carlo (MCMC) sampler, performs<br/> well in retrieving known parameter values from virtual<br/> inventory data generated by the forest model. We analyze<br/> the results of the parameter estimation, examine its sensitivity<br/> to the choice and aggregation of model outputs and<br/> observed data (summary statistics), and demonstrate the application<br/> of this method by fitting the FORMIND model to<br/> field data from an Ecuadorian tropical forest. Finally, we discuss<br/> how this approach differs from approximate Bayesian<br/> computation (ABC), another method commonly used to generate<br/> simulation-based likelihood approximations.<br/> Our results demonstrate that simulation-based inference,<br/> which offers considerable conceptual advantages over more<br/> traditional methods for inverse parameter estimation, can be<br/> successfully applied to process-based models of high complexity.<br/> The methodology is particularly suitable for heterogeneous<br/> and complex data structures and can easily be adjusted<br/> to other model types, including most stochastic population<br/> and individual-based models. Our study therefore provides<br/> a blueprint for a fairly general approach to parameter<br/> estimation of stochastic process-based models.
Keywords:
| FORMIND | aboveground biomass | forest model |
Literature type specific fields:
ARTICLE
Journal: Biogeosciences
Volume: 11
Page Range: 1261-1272
Metadata Provider:
Individual: Andreas Huth
Contact:
Online Distribution:
Download File: http://www.lcrs.de/publications.do?citid=1370


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