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Figure 2 | BMC Bioinformatics

Figure 2

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

Figure 2

Error rate for different models on datasets I and II. Error rate resulting from the different models on dataset I (left) and II (right). When the total number of incorrect probe calls is considered, both parameter estimation procedures outperform TileMap on dataset I for cut-offs larger than 0.2. Both Baum-Welch and Viterbi training provide models with an optimal cut-off close to 0.5, while TileMap significantly underestimates the posterior probability resulting in an optimal cut-off of 0.19. The models with optimised parameters show similar performance on both datasets. On dataset II TileMap's performance is reduced in comparison to the results on dataset I. The main differences between the models considered here occur at error rates of 0–0.08. The relevant area of the figures in the top row is magnified in the plots below.

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