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

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 $1/ε$.

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