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Das Hidden-Markov-Modell
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Im Mittelpunkt dieses essentials steht eine Einfhrung in ein bekanntes statistisches Modell, das Hidden-Markov-Modell. Damit knnen Probleme bewltigt werden, bei denen aus einer Folge von Beobachtungen auf die wahrscheinlichste zustandsspezifische Beschreibung geschlossen werden soll. Die Anwendungen des Hidden-Markov-Modells liegen hauptschlich in den Bereichen Bioinformatik, Computerlinguistik, maschinelles Lernen und Signalverarbeitung. In diesem Bchlein werden die beiden zentralen Problemstellungen in HMMs behandelt. Das Problem der Inferenz wird mit dem berhmten Viterbi-Algorithmus gelst, und das Problem der Parameterschtzung wird mit zwei bekannten Methoden angegangen (Erwartungsmaximierung und Baum-Welch).
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