Scientists have built simulations to help explain behavior in the real world, including modeling for disease transmission and prevention, autonomous vehicles, climate science, and in the search for the fundamental secrets of the universe. But how to interpret vast volumes of experimental data in terms of these detailed simulations remains a key challenge. Probabilistic programming offers a solution—essentially reverse-engineering the simulation—but this technique has long been limited due to the need to rewrite the simulation in custom computer languages, plus the intense computing power required.
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Source: Phys.org