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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. |author2=Shani A. |author3=Gohr A. |author4=Grau J. |author5=Arviv S. |author6=Shmilovici A. |author7=Posch S. |author8=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|doi-access = free}}</ref><ref name="Grau">{{cite journal|last = Grau|first = J.|author2=Ben-Gal I. |author3=Posch S. |author4=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) models, [[Markov model]]s, and Bayesian network (BN) models.
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