Computing bounds for entropy of stationary Z^d Markov random fields

Abstract

For any stationary $\mZ^d$-Gibbs measure that satisfies strong spatial mixing, we obtain sequences of upper and lower approximations that converge to its entropy. In the case, $d=2$, these approximations are efficient in the sense that the approximations are accurate to within $\epsilon$ and can be computed in time polynomial in $1/\epsilon$.

This is a revision of paper originally posted in April, 2012

References in corpus (1)

• Strong spatial mixing for list coloring of graphs