A hidden Markov model (HMM) is one in which you observe a sequence of emissions, but do not know the sequence of states the model went through to generate the emissions. Analyses of hidden Markov models seek to recover the sequence of states from the observed data. (Source:

References in zbMATH (referenced in 11 articles )

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  1. Douc, Randal; Moulines, Eric; Stoffer, David S.: Nonlinear time series. Theory, methods and applications with R examples (2014)
  2. Fox, Emily B.; Hughes, Michael C.; Sudderth, Erik B.; Jordan, Michael I.: Joint modeling of multiple time series via the beta process with application to motion capture segmentation (2014)
  3. Schmidt, Miriam; Palm, G√ľnther; Schwenker, Friedhelm: Spectral graph features for the classification of graphs and graph sequences (2014)
  4. Daly, Ian; Sweeney-Reed, Catherine M.; Nasuto, Slawomir J.: Testing for significance of phase synchronisation dynamics in the EEG (2013)
  5. Daly, Ian; Nasuto, Slawomir J.; Warwick, Kevin: Brain computer interface control via functional connectivity dynamics (2012) ioport
  6. Nguyen, Tan Dat; Ranganath, Surendra: Facial expressions in American sign language: tracking and recognition (2012)
  7. Pirri, Fiora: The well-designed logical robot: learning and experience from observations to the Situation Calculus (2011)
  8. Mitra, Sovan; Date, Paresh: Regime switching volatility calibration by the Baum-Welch method (2010)
  9. Mitra, Sovan; Ji, Tong: Optimisation of stochastic programming by hidden Markov modelling based scenario generation (2010)
  10. Skowronski, Mark D.; Harris, John G.: Automatic speech recognition using a predictive echo state network classifier (2007)
  11. Lee, Jason; Wong, Lee: A stochastic model for the detection of coherent motion (2004)