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

BACKGROUND: 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...

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Detalles Bibliográficos
Autores principales: Lam, Tin Y, Meyer, Irmtraud M
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3019189/
https://www.ncbi.nlm.nih.gov/pubmed/21143925
http://dx.doi.org/10.1186/1748-7188-5-38

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3019189/
https://www.ncbi.nlm.nih.gov/pubmed/21143925
http://dx.doi.org/10.1186/1748-7188-5-38

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