Large deviations estimates for semilinear stochastic equations

2006 ◽  
pp. 218-231 ◽  
Author(s):  
W. Smoleński ◽  
R. Sztencel ◽  
J. Zabczyk
Keyword(s):  
Large Deviations ◽  
Stochastic Equations ◽  
Large Deviations Estimates

scholarly journals The height of random binary unlabelled trees

2008 ◽  
Vol DMTCS Proceedings vol. AI,... (Proceedings) ◽  
Author(s):  
Nicolas Broutin ◽  
Philippe Flajolet
Keyword(s):  
Large Deviations ◽  
Complex Plane ◽  
Generating Functions ◽  
Binary Trees ◽  
International Audience ◽  
Large Deviations Estimates

International audience This extended abstract is dedicated to the analysis of the height of non-plane unlabelled rooted binary trees. The height of such a tree chosen uniformly among those of size $n$ is proved to have a limiting theta distribution, both in a central and local sense. Moderate as well as large deviations estimates are also derived. The proofs rely on the analysis (in the complex plane) of generating functions associated with trees of bounded height.

scholarly journals Large deviations estimates for some white noise distributions

2014 ◽  
Vol 8 (4) ◽  
Author(s):  
Sonia Chaari ◽  
Achref Majid ◽  
Habib Ouerdiane
Keyword(s):  
Large Deviations ◽  
White Noise ◽  
Large Deviations Estimates

scholarly journals Large deviations estimates for the multiscale analysis of heart rate variability

2012 ◽  
Vol 391 (22) ◽  
pp. 5658-5671 ◽  
Author(s):  
Patrick Loiseau ◽  
Claire Médigue ◽  
Paulo Gonçalves ◽  
Najmeddine Attia ◽  
Stéphane Seuret ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Large Deviations ◽  
Multiscale Analysis ◽  
Large Deviations Estimates

scholarly journals Erdős–Rényi law of large numbers in the averaging setup

2018 ◽  
Vol 18 (03) ◽  
pp. 1850018
Author(s):  
Yuri Kifer
Keyword(s):  
Dynamical Systems ◽  
Large Deviations ◽  
Stochastic Processes ◽  
Continuous Time ◽  
Law Of Large Numbers ◽  
Large Numbers ◽  
Large Deviations Estimates ◽  
Fast Motions

We extend the Erdős–Rényi law of large numbers to the averaging setup both in discrete and continuous time cases. We consider both stochastic processes and dynamical systems as fast motions whenever they are fast mixing and satisfy large deviations estimates. In the continuous time case we consider flows with large deviations estimates which allow a suspension representation and it turns out that fast mixing of corresponding base transformations suffices for our results.

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