Statistical Inference in Hidden Markov Models Using k-Segment Constraints

Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence o...

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Detalles Bibliográficos
Autores principales: Titsias, Michalis K., Holmes, Christopher C., Yau, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2016
Materias:
Theory and Methods
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4867884/
https://www.ncbi.nlm.nih.gov/pubmed/27226674
http://dx.doi.org/10.1080/01621459.2014.998762

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4867884/
https://www.ncbi.nlm.nih.gov/pubmed/27226674
http://dx.doi.org/10.1080/01621459.2014.998762

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