By Neal P.
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Additional info for A case study in non-centering for data augmentation: Stochastic epidemics
In practical terms, this meant using the laws of chance to describe how objects behaved on the average, rather than knowing, with certainty, the behavior of every object in a system. For exam ple, to develop laws describing thermodynamics and the motion of gases, nineteenth-century physicists made the assumption that molecules moved randomly. This was called Brownian motion, after biologist Robert Brown, who noticed the phenomenon in 1 82 1 . This assumption allowed develop ment of useful mathematical models based on randomness that provided facts about the average motion of many mole cules, rather than unknown, deterministic facts about the motions of individual molecules.
This is extremely tedious to do manually, even in a limited way. For example , to find how the Denver Broncos did on Sunday during the first four weeks of the season, you would have to tally the results of many seasons, somehow uncovering the interesting pattern that arose during the first four weeks . In automatically finding patterns, the computer " thinks " of things that we would never think of ourselves , even if we had the time . The software doing this search discovered and analyzed almost a million patterns per hour.
It wouldn't have made much sense for ancient sages to attempt to compute the probability that messages they received from the gods were correct. The gods were always correct, and the sages were the liaisons between the gods and the people . Thus, if the sages wanted to keep their j obs, it was important that the advice they obtained from the gods was phrased so that it was , in fact, always correct. This resulted in metaphorical messages and carefully crafted ora cles that were so general they would always be true .