hidden Markov model

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hidden Markov model

[¦hid·ən ′mär·kəf ‚mäd·əl]
(mathematics)
A finite-state machine that is also a doubly stochastic process involving at least two levels of uncertainty: a random process associated with each state, and a Markov chain, which characterizes the probabilistic relationship among the states in terms of how likely one state is to follow another.
References in periodicals archive ?
2010: depmixS4: An R package for hidden Markov models.
In [24], the authors analyzed features from body joints from noisy depth images where stereo cameras were deployed for obtaining depth images and then, the features were applied with hidden Markov models (HMMs) for activity recognition.
2013) compared different techniques of action recognition using Hidden Markov Models (HMM).
These algorithms can be translated into software via hierarchical hidden Markov models, and Kurzweil demonstrates that these models can be used to perform speech recognition and query analysis.
Hidden Markov models (HMM) have been used to inspect ECG waveforms to find abnormal heart rhythm [5].
Modeling cognitive-affective dynamics with Hidden Markov Models.
At that early stage, neural network based speech recognition was radically different from Hidden Markov Models (HMMs) based speech recognition.
For this purpose, some systems [4] use dynamic programming- (DP-) matching [7], whereas others [5, 8, 9] use hidden Markov models (HMMs) [10].
Hidden Markov models (HMMs) are widely used to model the maneuvering process of an agent.
The author has arranged the bulk of his text in eight chapters, providing an introduction to the gospels and the synoptic problem, covering the triple-link model, MatthewAEs and LukeAEs use of the gospel according to Mark, hidden Markov models for binary time series, and several other interconnected subjects.
Neural networks, Bayesian and quantum probability models, Act-R, and hidden Markov models were some of the approaches proposed for a wealth of cognitive processes.