Marking (1, 2) points of the Brownian web and applications

C. M. Newman, K. Ravishankar, E. Schertzer

Research output: Contribution to journalArticlepeer-review


The Brownian web (BW), which developed from the work of Arratia and then Tóth and Werner, is a random collection of paths (with specified starting points) in one plus one dimensional space-time that arises as the scaling limit of the discrete web (DW) of coalescing simple random walks. Two recently introduced extensions of the BW, the Brownian net (BN) constructed by Sun and Swart, and the dynamical Brownian web (DyBW) proposed by Howitt and Warren, are (or should be) scaling limits of corresponding discrete extensions of the DW - the discrete net (DN) and the dynamical discrete web (DyDW). These discrete extensions have a natural geometric structure in which the underlying Bernoulli left or right "arrow" structure of the DWis extended by means of branching (i.e., allowing left and right simultaneously) to construct the DN or by means of switching (i.e., from left to right and vice-versa) to construct the DyDW. In this paper we show that there is a similar structure in the continuum where arrow direction is replaced by the left or right parity of the (1, 2) space-time points of the BW (points with one incoming path from the past and two outgoing paths to the future, only one of which is a continuation of the incoming path). We then provide a complete construction of the DyBW and an alternate construction of the BN to that of Sun and Swart by proving that the switching or branching can be implemented by a Poissonian marking of the (1, 2) points.

Original languageEnglish (US)
Pages (from-to)537-574
Number of pages38
JournalAnnales de l'institut Henri Poincare (B) Probability and Statistics
Issue number2
StatePublished - May 2010


  • Brownian net
  • Brownian web
  • Coalescing random walks
  • Dynamical Brownian web
  • Nucleation on boundaries
  • Poissonian marking
  • Sticky Brownian motion

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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