The algorithm was developed by applying scientific principles used to create models for understanding cell biology and physics to the challenges of cosmology and big data.
"Science works because things behave much more simply than they have any right to," said professor of physics James Sethna. "Very complicated things end up doing rather simple collective behavior."
Sethna - Author - Visualizing - Probabilistic - Models
Sethna is the senior author of "Visualizing Probabilistic Models With Intensive Principal Component Analysis," published in the Proceedings of the National Academy of Sciences.
The algorithm, designed by first author Katherine Quinn, allows researchers to image a large set of probabilities to look for patterns or other information that might be useful, and provides them with better intuition for understanding complex models and data.
Person - Quinn - Algorithm - Way - Information
"A person can't just sit down and do it," Quinn said. "We need better algorithms that can extract what we're interested in, without being told what to look for. We can't just say, 'Look for interesting universes.' This algorithm is a way of untangling information in a way that can reveal the interesting structure of the data."
Further complicating the researchers' task was the fact that the data consists of ranges of probabilities, rather than raw images or numbers.
Their solution takes...
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