TG14 – MCMC algorithm comparison

TG Leader: Francesco Minunno
Description: The aim of this TG is to compare different algorithms for the calibration of forest models. The work will benefit from the expertises of the members of the group and the experience that they already had in calibrating their models. This TG will define and review the different classes of algorithms that are currently used or that could be potentially used for forest model calibration, e.g. Markov chain Monte Carlo simulations (MCMC), sequential Monte Carlo methods (SMC), optimization algorithms (OA), emulators. The output of this work will be a review paper on how to choose the most appropriate method for forest model calibration in different context s (to be further discussed). A training school will be organized in the coming Spring, and it would provide the possibility to define models and data that could be suitable for the comparative analysis. A paper will be published about the results of this analysis. Additionally, this TG will develop a R package for calibration of forest models and provide a tutorial with codes and examples to run the algorithms.

Products: Review paper, Comparative analysis paper, R package, Tutorial


Participation: If you are interested in participating in the task group, please contact the task group coordinator directly. If you are interested in the working group in general, please contact the working group leaders.