Variable-order Markov model: Difference between revisions

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'''Variable order Markov (VOM)''' 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'' and thus the VOM models are also called ''Context Trees'' [1]. 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.

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Various efficient algorithms were devised for estimating the parameters of the VOM model [3]. The VOM models were successfully applied to areas such as Machine learning, Information theory and Bioinformatics including specific applications such as coding and data compression [1] document compression [3], classification and identification of DNA and protein sequences [2] Statistical Process Control [4] and more.

==See Alsoalso==

• Markov Chains

• Examples of Markov chains

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[2] Shmilovici A., Ben-Gal I. (2007), “Using a VOM Model for Reconstructing Potential Coding Regions in EST Sequences”, accepted to Computational Statistics, forthcoming.<br />

[3] Begleiter R., El-Yaniv R., Yona G., (2004), "On Prediction Using Variable Order Markov Models", Journal of Artificial Intelligence, 22:385-421.<br />

[4] Ben-Gal I., Morag G., Shmilovici A., (2003) "CSPC: A Monitoring Procedure for State Dependent Processes", Technometrics, vol. 45, no. 4, pp. 293-311.<br />