  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.
