A Lower Bound on the Complexity of Approximating the Entropy of a Markov Source

Suppose that, for any (k \geq 1), (\epsilon > 0) and sufficiently large $\sigma$, we are given a black box that allows us to sample characters from a $k$th-order Markov source over the alphabet ({0, …, \sigma - 1}). Even if we know the source has entropy either 0 or at least (\log (\sigma - k)), there is still no algorithm that, with probability bounded away from (1 / 2), guesses the entropy correctly after sampling at most ((\sigma - k)^{k / 2 - \epsilon}) characters.