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Table 1 AUC for different models on both simulated datasets.

From: Parameter estimation for robust HMM analysis of ChIP-chip data

  TileMap Baum-Welch Viterbi-Training Viterbi-EM ad hoc
dataset I 0.9986 0.9998 0.9997 0.9998 0.9869
dataset II 0.9749 0.9995 0.9994 0.9995 0.9728
  1. All models with optimised parameters outperform TileMap on both simulated datasets. While TileMap performs well on dataset I it is only slightly better than the model with ad hoc parameter estimates.