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Hidden Markov Model
CHF 161.95
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SKU
6L6213HSKUE
1 Verfügbar 
Geliefert zwischen Mo., 04.05.2026 und Di., 05.05.2026
Details
A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with unobserved state. An HMM can be considered as the simplest dynamic Bayesian network. In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters. In a hidden Markov model, the state is not directly visible, but output dependent on the state is visible. Each state has a probability distribution over the possible output tokens. Therefore the sequence of tokens generated by a HMM gives some information about the sequence of states. Note that the adjective 'hidden' refers to the state sequence through which the model passes, not to the parameters of the model; even if the model parameters are known exactly, the model is still 'hidden'. Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition, part-of-speech tagging, musical score following, partial discharges and bioinformatics.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09786130276126
- Editor Frederic P. Miller, Agnes F. Vandome, John McBrewster
- Sprache Englisch
- Größe H220mm x B150mm x T7mm
- Jahr 2010
- EAN 9786130276126
- Format Fachbuch
- ISBN 978-613-0-27612-6
- Titel Hidden Markov Model
- Untertitel Markov process, Markov chain, Time, Speech recognition, Handwriting recognition, Gesture recognition, Part-of-speech tagging, Sheet music, Partial discharge, Bioinformatics, Bayesian inference
- Gewicht 203g
- Herausgeber Alphascript Publishing
- Anzahl Seiten 124
- Genre Biologie
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