Variable-order Markov Model : Markov Chain, Markov Property, Andrey Markov, Probability Theory, Conditional Probability Distribution, Continuous-time Markov Process, Markov Process, Random Variable

Lambert M. Surhone

ISBN 10: 6130335393 / ISBN 13: 9786130335397

Verlag: Omniscriptum, 2010

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! Variable-order Markov models are an important class of models that extend the well known Markov chain models. In contrast to the Markov chain models, where each random variable in a sequence with a Markov property depends on a fixed number of random variables, in VOM models this number of conditioning random variables may vary based on the specific observed realization. This realization sequence is often called the context; therefore the VOM models are also called context trees. The flexibility in the number of conditioning random variables turns out to be of real advantage for many applications, such as statistical analysis, classification and prediction.

Bestandsnummer des Verkäufers 9786130335397

Bibliografische Details

• Titel: Variable-order Markov Model : Markov Chain, Markov Property, Andrey Markov, Probability Theory, Conditional Probability Distribution, Continuous-time Markov Process, Markov Process, Random Variable
• Verlag: Omniscriptum
• Erscheinungsjahr: 2010
• Sprache: Englisch
• ISBN-10: 6130335393
• ISBN-13: 9786130335397
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 220B x 150 H x 5 T Millimeter Breite: 220 mm, Höhe: 150 mm, Tiefe: 5 mm
• Gewicht: 137 Gramm

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Inhaltsangabe

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Variable-order Markov models are an important class of models that extend the well known Markov chain models. In contrast to the Markov chain models, where each random variable in a sequence with a Markov property depends on a fixed number of random variables, in VOM models this number of conditioning random variables may vary based on the specific observed realization. This realization sequence is often called the context; therefore the VOM models are also called context trees. The flexibility in the number of conditioning random variables turns out to be of real advantage for many applications, such as statistical analysis, classification and prediction.