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What if we could make predictions for experimental measurements at the Large Hadron Collider based entirely on first-principles theoretical calculations? While this dream is hopelessly out of reach, we do have a growing catalog of precision calculations in quantum chromodynamics (QCD) as a well as increasingly accurate Monte Carlo generators. In this talk, I show how to leverage ideas from information theory and machine learning to merge these disparate QCD predictions into a unified theoretical prediction with associated uncertainties. Our strategy highlights the importance of logarithmic moments, which have not been previously studied in the QCD literature, either experimentally or theoretically.