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Spatializing random measures: Doubly indexed processes and the large deviation principle
  • C Boucher
  • RS Ellis, University of Massachusetts - Amherst
  • B Turkington
Publication Date
The main theorem is the large deviation principle for the doubly indexed sequence of random measures Abstract Here $\theta$ is a probability measure on a Polish space $\mathscr{X},{D_{r,k}k=1,\ldots,2^r}$ is a dyadic partition of $\mathscr{X}$ (hence the use of $2^r$ summands) satisfying $\theta{D_{r,k}}= 1/2^r$ and $L_{q,1}L_{q,2},\ldotsL_{q,2_r}$ is an independent, identically distributed sequesnce of random probability measures on a Ploish space$ \mathscr{Y}$ such that ${L_{q,k}q\in \mathsbb{N}}$ satisfies the large deviation principle with a convex rate function. A number of related asymptotic results are also derived. The random measures $W_{ r,q}$ have important applications to the statistical mechanics of turbulence. In a companion paper, the large deviation principle presented here is used to give a rigorous derivation of maximum entropy principles arising in the well-known Miller–Robert theory of two-dimensional turbulence as well as in a modification of that theory recently proposed by Turkington.
Citation Information
C Boucher, RS Ellis and B Turkington. "Spatializing random measures: Doubly indexed processes and the large deviation principle" ANNALS OF PROBABILITY Vol. 27 Iss. 1 (1999)
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