Universal Estimation of Erasure Entropy
Erasure entropy rate differs from Shannon's entropy rate in that the conditioning occurs with respect to both the past and the future, as opposed to only the past (or the future). In this paper, consistent universal algorithms for estimating erasure entropy rate are proposed based on the basic and extended context-tree weighting (CTW) algorithms. Simulation results for those algorithms applied to Markov sources, tree sources, and English texts are compared to those obtained by fixed-order plug-in estimators with different orders.
entropy rate universal algorithms context-tree weighting bidirectional context tree data compression universal modeling