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Two case studies detailing Bayesian parameter inference for dynamic energy budget models
[article]
2018
bioRxiv
pre-print
Mechanistic representations of individual life-history trajectories are powerful tools for the prediction of organismal growth, reproduction and survival under novel environmental conditions. Dynamic energy budget (DEB) theory provides compact models to describe the acquisition and allocation of energy by organisms over their full life cycle. However, estimating DEB model parameters, and their associated uncertainties and covariances, is not trivial. Bayesian inference provides a coherent way
doi:10.1101/259705
fatcat:4elprso45venxbpis5jz3e7qke