Computing bounds for entropy of stationary Z^d Markov random fields
Brian H. Marcus, Ronnie Pavlov · arXiv (Cornell University) · 2012
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 $ε$ and can be computed in time polynomial in $1/ε$.