Metrics and Similarity Measures for Hidden Markov Models

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, methodology): “Quite a few PSSM-based profile comparison methods for homology detection have been developed, including [11, 18-23].”
Bhattacharyya and Expected Likelihood Kernels (2003, methodology): “The expected likelihood kernel is merely the co-emission probability of two different HMMs [19, 14].”
A bibliographical study of grammatical inference (2005, unknown): “study all typical distances between distributions in [174] and prove intractability results in[175].”
Within the twilight zone: a sensitive profile-profile comparison tool based on information theory. (2002, methodology): “Lyngso et al.(19) used the co-emission probability of two pro®le hidden Markov models to measure their similarity.”
Probabilistic finite-state machines - part I (2005, background): “This product is called the coemission probability of x by A1 and A2 [16].”
Finding weak similarities between proteins by sequence profile comparison. (2003, background): “More sophisticated measures of comparing two pro®les or HMMs have been proposed recently and proved to be successful in detecting weak similarities between conserved protein regions or for the classi®cation of signal peptides (14,15).”