2023
DOI: 10.5194/gmd-2022-302-supplement
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Supplementary material to "Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive MCMC algorithm"

Abstract: The Adaptive Metropolis algorithm used here contains three key concepts explained in the following sections. The adaptive random walk, allowing the algorithm to learn features of the target distribution; transformed proposals, providing a natural way of including limits on the parameters; and tempering of the target distribution, to reduce the effects of local maxima and allowing better exploration of the target.

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