A fundamental challenge in constructing Big Models is the question of calibration. Big Models typically have a large number of parameters, which need to be inferred from data in a robust and theoretically justifiable way. Approximate Bayesian Computation is one promising approach to tackle this calibration problem for large complex models.
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