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Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training
2010
Algorithms for Molecular Biology
Hidden Markov models are widely employed by numerous bioinformatics programs used today. Applications range widely from comparative gene prediction to time-series analyses of micro-array data. The parameters of the underlying models need to be adjusted for specific data sets, for example the genome of a particular species, in order to maximize the prediction accuracy. Computationally efficient algorithms for parameter training are thus key to maximizing the usability of a wide range of
doi:10.1186/1748-7188-5-38
pmid:21143925
pmcid:PMC3019189
fatcat:ybcmj27t5jc2bphsvwc2hs3hni