On the Contraction Method with Reduced Independence Assumptions

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: On the Contraction Method with Reduced Independence Assumptions
Συγγραφείς: Neininger, Ralph, Straub, Jasmin
Συνεισφορές: Ralph Neininger and Jasmin Straub
Στοιχεία εκδότη: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2022.
Έτος έκδοσης: 2022
Θεματικοί όροι: Contraction Method, weak Convergence, random Trees, Probabilistic Analysis of Algorithms, ddc:004, Probability Metrics
Περιγραφή: Recursive sequences of laws of random variables (and random vectors) are considered where an independence assumption which is usually made within the setting of the contraction method is dropped. This restricts the study to sequences which after normalization lead to asymptotic normality. We provide a general univariate central limit theorem which can directly be applied to problems from the analysis of algorithms and random recursive structures without further knowledge of the contraction method. Also multivariate central limit theorems are shown and bounds on rates of convergence are provided. Examples include some previously shown central limit analogues as well as new applications on Fibonacci matchings.
Τύπος εγγράφου: Conference object
Article
Περιγραφή αρχείου: application/pdf
Γλώσσα: English
DOI: 10.4230/lipics.aofa.2022.14
Σύνδεσμος πρόσβασης: https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AofA.2022.14
Rights: CC BY
Αριθμός Καταχώρησης: edsair.dedup.wf.002..a74c8c672dd8c363312ba23bce3430ce
Βάση Δεδομένων: OpenAIRE
Περιγραφή
DOI:10.4230/lipics.aofa.2022.14