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Table 2 Improvements of cBW + model selection, cBW alon vs Scheffer et al., with fully connected initial transition matrix for synthetic data with 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 7.35/8.29% 2.1E−02/6.2E−05 7.61/8.49%
3/2000 7.99/8.82% 3.9E−02/2.6E−09 8.31/9.00%
3/2400 8.47/9.13% 2.8E−03/2.4E−10 8.71/9.18%
3/2800 8.51/9.24% 5.8E−03/6.9E−10 8.65/9.24%
5/1600 12.97/14.75% 7.8E−05/3.3E−05 11.13/12.82%
5/2000 14.63/16.32% 2.5E−03/1.2E−06 12.65/14.11%
5/2400 14.69/16.54% 7.8E−04/6.2E−06 12.73/14.50%
5/2800 15.56/17.22% 1.6E−02/1.3E−07 13.72/15.22%
7/1600 8.61/10.42% 3.5E−02/1.1E−02 5.56/7.20%
7/2000 10.56/12.40% 6.4E−03/6.2E−03 7.87/9.52%
7/2400 11.35/13.21% 8.2E−03/7.8E−03 8.71/10.43%
7/2800 12.16/14.06% 1.0E−03/1.1E−04 9.68/11.41%