Regenerative tree growth: Binary self-similar continuum random trees and Poisson--Dirichlet compositions

Mathematics – Probability

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Published in at http://dx.doi.org/10.1214/08-AOP445 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of

Scientific paper

10.1214/08-AOP445

We use a natural ordered extension of the Chinese Restaurant Process to grow a two-parameter family of binary self-similar continuum fragmentation trees. We provide an explicit embedding of Ford's sequence of alpha model trees in the continuum tree which we identified in a previous article as a distributional scaling limit of Ford's trees. In general, the Markov branching trees induced by the two-parameter growth rule are not sampling consistent, so the existence of compact limiting trees cannot be deduced from previous work on the sampling consistent case. We develop here a new approach to establish such limits, based on regenerative interval partitions and the urn-model description of sampling from Dirichlet random distributions.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Regenerative tree growth: Binary self-similar continuum random trees and Poisson--Dirichlet compositions does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Regenerative tree growth: Binary self-similar continuum random trees and Poisson--Dirichlet compositions, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Regenerative tree growth: Binary self-similar continuum random trees and Poisson--Dirichlet compositions will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-586961

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.