Calibration of a bumble bee foraging model using Approximate Bayesian Computation

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

Ecological Modelling, 2023, vol. 477, issue C

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.

Keywords: Approximate Bayesian Computation; Foraging model; Calibration; Pollination (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:477:y:2023:i:c:s0304380022003490

DOI: 10.1016/j.ecolmodel.2022.110251

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