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UID:5886@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20221115T143000
DTEND;TZID=Europe/Paris:20221115T153000
DTSTAMP:20241120T200647Z
URL:https://www.i2m.univ-amu.fr/evenements/statistical-limits-of-correlati
 on-detection-in-trees/
SUMMARY:Luca Ganassali (INRIA Paris): Statistical limits of correlation det
 ection in trees
DESCRIPTION:Luca Ganassali: L. Ganassali\, L. Massoulié\, G. Semerjian. St
 atistical limits of correlation detection in trees\, 2022\, submitted.\n[a
 rXiv]\nShort abstract: In this paper we address the problem of testing whe
 ther two observed trees are sampled either independently or from a joint d
 istribution under which they are correlated. This problem\, which we refer
  to as correlation detection in trees\, plays a key role in the study of g
 raph alignment for two correlated random graphs. Motivated by graph alignm
 ent\, we investigate the conditions of existence of one-sided tests\, i.e.
  tests which have vanishing type I error and non-vanishing power in the li
 mit of large tree depth. For the correlated Galton-Watson model we identif
 y a phase transition in the limit of large degrees: we prove that no such 
 test exists below this threshold and that such a test exists whenever abov
 e the threshold\, for a large enough mean degree.\n
ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2
 022/09/Luca_Ganassali.png
CATEGORIES:Séminaire,Probabilités
LOCATION:I2M Chateau-Gombert - CMI\, Salle de Séminaire R164 (1er étage)\
 , 39 Rue Joliot Curie\, 13013 Marseille\, France\, Campus Château-Gombert
 \, 
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