5 research outputs found
Asymptotic evolution of acyclic random mappings
An acyclic mapping from an element set into itself is a mapping
such that if for some and , then .
Equivalently, for sufficiently large.
We investigate the behavior as of a Markov chain on the
collection of such mappings. At each step of the chain, a point in the
element set is chosen uniformly at random and the current mapping is modified
by replacing the current image of that point by a new one chosen independently
and uniformly at random, conditional on the resulting mapping being again
acyclic. We can represent an acyclic mapping as a directed graph (such a graph
will be a collection of rooted trees) and think of these directed graphs as
metric spaces with some extra structure. Heuristic calculations indicate that
the metric space valued process associated with the Markov chain should, after
an appropriate time and ``space'' rescaling, converge as to a
real tree (-tree) valued Markov process that is reversible with respect to
a measure induced naturally by the standard reflected Brownian bridge. The
limit process, which we construct using Dirichlet form methods, is a Hunt
process with respect to a suitable Gromov-Hausdorff-like metric. This process
is similar to one that appears in earlier work by Evans and Winter as the limit
of chains involving the subtree prune and regraft tree (SPR) rearrangements
from phylogenetics.Comment: 26 pages, 4 figure