90 research outputs found
Tight Markov chains and random compositions
For an ergodic Markov chain on , with a stationary
distribution , let denote a hitting time for , and let
. Around 2005 Guy Louchard popularized a conjecture that, for , is almost Geometric(), , is almost
stationarily distributed on , and that and are almost
independent, if exponentially fast. For the
chains with however slowly, and with
, we show that Louchard's
conjecture is indeed true even for the hits of an arbitrary
with . More precisely, a sequence of consecutive hit
locations paired with the time elapsed since a previous hit (for the first hit,
since the starting moment) is approximated, within a total variation distance
of order , by a -long sequence of independent copies of
, where , is
distributed stationarily on , and is independent of . The
two conditions are easily met by the Markov chains that arose in Louchard's
studies as likely sharp approximations of two random compositions of a large
integer , a column-convex animal (cca) composition and a Carlitz (C)
composition. We show that this approximation is indeed very sharp for most of
the parts of the random compositions. Combining the two approximations in a
tandem, we are able to determine the limiting distributions of
and largest parts of the random cca composition and the
random C-composition, respectively. (Submitted to Annals of Probability in
August, 2009.
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