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Hidden Markov model information


A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or "hidden") Markov process (referred to as ). An HMM requires that there be an observable process whose outcomes depend on the outcomes of in a known way. Since cannot be observed directly, the goal is to learn about state of by observing By definition of being a Markov model, an HMM has an additional requirement that the outcome of at time must be "influenced" exclusively by the outcome of at and that the outcomes of and at must be conditionally independent of at given at time Estimation of the parameters in an HMM can be performed using maximum likelihood. For linear chain HMMs, the Baum–Welch algorithm can be used to estimate the parameters.

Hidden Markov models are known for their applications to thermodynamics, statistical mechanics, physics, chemistry, economics, finance, signal processing, information theory, pattern recognition—such as speech,[1] handwriting, gesture recognition,[2] part-of-speech tagging, musical score following,[3] partial discharges[4] and bioinformatics.[5][6]

  1. ^ "Google Scholar".
  2. ^ Thad Starner, Alex Pentland. Real-Time American Sign Language Visual Recognition From Video Using Hidden Markov Models. Master's Thesis, MIT, Feb 1995, Program in Media Arts
  3. ^ B. Pardo and W. Birmingham. Modeling Form for On-line Following of Musical Performances Archived 2012-02-06 at the Wayback Machine. AAAI-05 Proc., July 2005.
  4. ^ Satish L, Gururaj BI (April 2003). "Use of hidden Markov models for partial discharge pattern classification". IEEE Transactions on Dielectrics and Electrical Insulation.
  5. ^ Li, N; Stephens, M (December 2003). "Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data". Genetics. 165 (4): 2213–33. doi:10.1093/genetics/165.4.2213. PMC 1462870. PMID 14704198.
  6. ^ Ernst, Jason; Kellis, Manolis (March 2012). "ChromHMM: automating chromatin-state discovery and characterization". Nature Methods. 9 (3): 215–216. doi:10.1038/nmeth.1906. PMC 3577932. PMID 22373907.

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