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'''Variable-order Bayesian network (VOBN)''' models provide an important extension of both the [[Bayesian network]] models and the [[variable-order Markov models]]. VOBN models are used in [[machine learning]] in general and have shown great potential in [[bioinformatics]] applications.<ref name="Ben-Gal">{{cite journal|last = Ben-Gal|first = I.|coauthors = Shani A., Gohr A., Grau J., Arviv S., Shmilovici A., Posch S. and Grosse I.|title = Identification of Transcription Factor Binding Sites with Variable-order Bayesian Networks|journal = Bioinformatics|volume = 21|issue = 11|date = 2005|pages = 2657–2666|url = http://bioinformatics.oxfordjournals.org/cgi/reprint/bti410?ijkey=KkxNhRdTSfvtvXY&keytype=ref|doi = 10.1093/bioinformatics/bti410|pmid = 15797905}}</ref><ref name="Grau">{{cite journal|last = Grau|first = J.|coauthors = Ben-Gal I., Posch S., Grosse I.|title = VOMBAT: Prediction of Transcription Factor Binding Sites using Variable Order Bayesian Trees|journal = Nucleic Acids Research |volume = 34|date = 2006|pages = 529–533|url = http://www.eng.tau.ac.il/~bengal/VOMBAT.pdf|doi = 10.1093/nar/gkl212|pmid = 16845064|issue = Web Server issue|pmc = 1538886}}</ref>
These models extend the widely-used [[position weight matrix]] (PWM)
In contrast to the BN models, where each random variable depends on a fixed subset of random variables, in VOBN models these subsets may vary based on the specific realization of observed variables. The observed realizations are often called the context and, hence, VOBN models are also known as context-specific Bayesian networks.<ref name=" Boutilier ">{{cite journal|last = Boutilier|first = C.|coauthors = Friedman N., Goldszmidt M., Koller D.|title = Context-specific independence in Bayesian networks|journal = In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence|date = August 1–4, 1996, Reed College, Portland,
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