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تسجيل مستخدم جديدDoes model-free forecasting really outperform the true model? A reply to Perretti et al
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Estimating population models from uncertain observations is an important problem in ecology. Perretti et al. observed that standard Bayesian state-space solutions to this problem may provide biased parameter estimates when the underlying dynamics are chaotic. Consequently, forecasts based on these estimates showed poor predictive accuracy compared to simple model-free methods, which lead Perretti et al. to conclude that Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data. However, a simple modification of the statistical methods also suffices to remove the bias and reverse their results.
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