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Complexity, Phase Transitions, and Inference by Cristopher Moore (part 1)

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Published on Jul 3, 2016

There is a deep analogy between statistical inference and statistical physics. I will give a friendly introduction to both of these fields. I will then discuss phase transitions in problems like community detection in networks, and clustering of sparse high-dimensional data, where if our data becomes too sparse or too noisy it becomes impossible to find the underlying pattern; moreover, I will discuss optimal algorithms that succeed as well as possible up to this point. Along the way, I will visit ideas from computational complexity, random graphs, random matrices, and spin glass theory.


This lecture is part of Games, Epidemics and Behavior

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