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Statistical physics of inference: thresholds and algorithms
Zdeborová, Lenka ; Krzakala, Florent
Advances in physics, 2016-09, Vol.65 (5), p.453-552
[Periódico revisado por pares]
Abingdon: Taylor & Francis
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Título:
Statistical physics of inference: thresholds and algorithms
Autor:
Zdeborová, Lenka
;
Krzakala, Florent
Assuntos:
64.60.aq networks
;
75.50.Lk magneticproperties of materials: spin glasses and other random magnets
;
89.70.Eg interdisciplinary: computational complexity
;
89.75.-k complex systems
;
Algorithms
;
Bayesian inference
;
belief propagation
;
compressed sensing
;
Estimating
;
Inference
;
Mathematical models
;
Networks
;
Phase transitions
;
phase transitions in computer science
;
Physics
;
Spin glass
;
spin glass theory
;
State of the art
;
Statistical inference
;
stochastic block model
;
Variables
É parte de:
Advances in physics, 2016-09, Vol.65 (5), p.453-552
Notas:
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição:
Many questions of fundamental interest in today's science can be formulated as inference problems: some partial, or noisy, observations are performed over a set of variables and the goal is to recover, or infer, the values of the variables based on the indirect information contained in the measurements. For such problems, the central scientific questions are: Under what conditions is the information contained in the measurements sufficient for a satisfactory inference to be possible? What are the most efficient algorithms for this task? A growing body of work has shown that often we can understand and locate these fundamental barriers by thinking of them as phase transitions in the sense of statistical physics. Moreover, it turned out that we can use the gained physical insight to develop new promising algorithms. The connection between inference and statistical physics is currently witnessing an impressive renaissance and we review here the current state-of-the-art, with a pedagogical focus on the Ising model which, formulated as an inference problem, we call the planted spin glass. In terms of applications we review two classes of problems: (i) inference of clusters on graphs and networks, with community detection as a special case and (ii) estimating a signal from its noisy linear measurements, with compressed sensing as a case of sparse estimation. Our goal is to provide a pedagogical review for researchers in physics and other fields interested in this fascinating topic.
Editor:
Abingdon: Taylor & Francis
Idioma:
Inglês
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