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 (u. a.)

ISBN 10: 6130335393 / ISBN 13: 9786130335397

Published by OmniScriptum, 2026

About this Item

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 (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130335397 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 101385136

Bibliographic Details

• Title: Variable-order Markov Model | Markov Chain, Markov Property, Andrey Markov, Probability Theory, Conditional Probability Distribution, Continuous-time Markov Process, Markov Process, Random Variable
• Publisher: OmniScriptum
• Publication Date: 2026
• Language: English
• ISBN 10: 6130335393
• ISBN 13: 9786130335397
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 137 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.