Timo Seppalainen
Entropy for Translation-Invariant Random-Cluster Measures
(108K, AMS-TeX)

ABSTRACT.  We study translation-invariant random-cluster measures 
with techniques from large deviation theory and convex 
analysis. In particular, we prove a large deviation principle 
with rate function given by a specific entropy, and a DLR 
variational principle that characterizes translation-invariant 
random-cluster measures as the solutions of the variational
equation for free energy. Consequences of these theorems 
include results about  edge and cluster densities of  
translation-invariant random-cluster measures.