Complexity, Phase Transitions, and Inference by Cristopher Moore (part 1)
Complexity, Phase Transitions, and Inference by Cristopher Moore (part 1)
Δημοσιεύτηκε στις 3 Ιουλ 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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