The Most Probable Labeling Problem in HMMs and
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The most probable annotation problem in HMMs and its application to bioinformatics
Hidden Markov models (HMMs) are often used for biological sequence annotation. Each sequence feature is represented by a collection of states with the same label. In annotating a new sequence, we seek the sequence of labels that has highest probability. Computing this most probable annotation was shown NP-hard by Lyngsø and Pedersen [15]. We improve their result by showing that the problem is N...
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