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Table 5 Improvements of cBW + model selection, cBW alone vs Scheffer et al. in cases of correct initial transition matrix for synthetic data with posterior-Viterbi algorithm

From: A new algorithm to train hidden Markov models for biological sequences with partial labels

State #/training sample Average improvements of cBW + model selection in setting 1/2 Average p value of cBW + model selection in setting 1/2 Average improvements of cBW alone in setting 1/2
3/1600 7.46/8.01% 2.8E−02/1.7E−02 7.64/8.11%
3/2000 8.09/8.52% 3.0E−02/1.6E−02 8.25/8.60%
3/2400 8.32/8.67% 3.7E−02/5.9E−03 8.49/8.73%
3/2800 8.58/8.99% 4.9E−02/1.2E−03 8.79/9.09%
5/1600 9.52/10.62% 1.1E−01/5.1E−02 9.45/10.59%
5/2000 10.53/11.55% 6.0E−02/8.2E−03 10.47/11.63%
5/2400 11.51/12.58% 2.0E−02/3.5E−04 11.44/12.58%
5/2800 12.49/13.55% 1.7E−02/8.0E−06 12.55/13.61%
7/1600 8.75/10.19% 2.9E−02/2.6E−02 8.27/9.77%
7/2000 10.50/11.99% 2.1E−02/4.2E−03 9.82/11.31%
7/2400 11.15/12.47% 6.9E−02/7.8E−04 10.69/12.11%
7/2800 12.36/13.75% 5.2E−02/1.1E−05 12.05/13.57%