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Table 4 Improvements of cBW + model selection, cBW alon vs Scheffer et al., with fully connected 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 7.08/8.02% 3.6E−02/1.9E−04 7.49/8.35%
3/2000 7.93/8.57% 9.5E−03/3.9E−05 8.32/8.88%
3/2400 8.21/8.85% 1.7E−03/2.5E−05 8.55/9.03%
3/2800 8.43/9.15% 1.8E−03/1.1E−06 8.82/9.36%
5/1600 9.13/10.62% 2.4E−02/3.2E−03 9.08/10.58%
5/2000 10.32/11.68% 1.4E−02/6.2E−05 10.38/11.76%
5/2400 11.26/12.74% 1.1E−02/2.0E−06 11.29/12.84%
5/2800 12.48/13.76% 9.8E−03/2.1E−08 12.56/13.86%
7/1600 8.40/10.08% 6.0E−02/2.5E−02 7.77/9.50%
7/2000 10.22/11.96% 3.2E−02/1.3E−04 9.82/11.65%
7/2400 11.10/12.68% 8.1E−03/1.5E−05 10.60/12.31%
7/2800 12.18/13.89% 1.6E−04/8.4E−08 11.96/13.77%