How to assess climate change impact models: uncertainty analysis of streamflow statistics via approximate Bayesian computation (ABC)

Romero-Cuéllar, Jonathan; Francés, Félix

doi:10.1080/02626667.2023.2231437

Cited by 1 publication

(1 citation statement)

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“…Approximate Bayesian Computation (ABC) is a widely utilized approach for simulation-based inference ( Marin et al., 2012 ; Romero-Cuellar and Francés, 2023 ). In ABC, the simulator generates synthetic data by sampling parameters from a prior distribution or proposal distribution and using these parameters to perform a simulation.…”

Section: Nine Simulation Intelligence Motifs For Plant Sciencementioning

confidence: 99%

Plant science in the age of simulation intelligence

Stock,

Pieters,

De Swaef

et al. 2024

Front. Plant Sci.

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Historically, plant and crop sciences have been quantitative fields that intensively use measurements and modeling. Traditionally, researchers choose between two dominant modeling approaches: mechanistic plant growth models or data-driven, statistical methodologies. At the intersection of both paradigms, a novel approach referred to as “simulation intelligence”, has emerged as a powerful tool for comprehending and controlling complex systems, including plants and crops. This work explores the transformative potential for the plant science community of the nine simulation intelligence motifs, from understanding molecular plant processes to optimizing greenhouse control. Many of these concepts, such as surrogate models and agent-based modeling, have gained prominence in plant and crop sciences. In contrast, some motifs, such as open-ended optimization or program synthesis, still need to be explored further. The motifs of simulation intelligence can potentially revolutionize breeding and precision farming towards more sustainable food production.

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Section: Nine Simulation Intelligence Motifs For Plant Sciencementioning

confidence: 99%