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Figure 3 | Algorithms for Molecular Biology

Figure 3

From: Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training

Figure 3

Parameter convergence for the dishonest casino. Average differences of the trained and known parameter values as function of the number of iterations for each training algorithm. For a given number of iterations, we first calculate the average value of the absolute differences between the trained and known value of each emission parameter (left figure) or transition parameter (right figure) and then take the average over the three experiments from the three-fold cross-evaluation. The error bars correspond to the standard deviation from the three cross-evaluation experiments. The algorithms have the same meaning as in Figure 2. Please refer to the text for more information.

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