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Calibration of a bumble bee foraging model using Approximate Bayesian Computation

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  • Baey, Charlotte
  • Smith, Henrik G.
  • Rundlöf, Maj
  • Olsson, Ola
  • Clough, Yann
  • Sahlin, Ullrika

Abstract

1. Challenging calibration of complex models can be approached by using prior knowledge on the parameters. However, the natural choice of Bayesian inference can be computationally heavy when relying on Markov Chain Monte Carlo (MCMC) sampling. When the likelihood of the data is intractable, alternative Bayesian methods have been proposed. Approximate Bayesian Computation (ABC) only requires sampling from the data generative model, but may be problematic when the dimension of the data is high.

Suggested Citation

  • Baey, Charlotte & Smith, Henrik G. & Rundlöf, Maj & Olsson, Ola & Clough, Yann & Sahlin, Ullrika, 2023. "Calibration of a bumble bee foraging model using Approximate Bayesian Computation," Ecological Modelling, Elsevier, vol. 477(C).
    • Handle: RePEc:eee:ecomod:v:477:y:2023:i:c:s0304380022003490
    DOI: 10.1016/j.ecolmodel.2022.110251
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    Cited by:

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    2. Walters, Richard J. & Olsson, Ola & Olsson, Peter & Smith, Henrik G., 2024. "Consequences of intraspecific competition for floral resources in heterogeneous landscapes for eusocial bees," Ecological Modelling, Elsevier, vol. 496(C).

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