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Models capable of capturing and reproducing the variability observed in experimental trials can be valuable for planning and control in the presence of uncertainty. This paper reports on a new data-driven methodology that extends deterministic models to a stochastic regime and offers probabilistic guarantees. From an acceptable deterministic model, a stochastic one is generated, capable of capturing and reproducing uncertain system-environment interactions at given levels of fidelity. Thedoi:10.1177/0278364915576336 fatcat:motijrfnonaejhaa4qvyhtmegi