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'''Variable-order Bayesian Networknetwork (VOBN)''' models provide an important extension of both the [[Bayesian Networknetwork]] models and the [[Variable variable-order Markov models]]. VOBN models are used in [[Machinemachine learning]] in general and have shown great potential in [[Bioinformaticsbioinformatics]] applications.<ref name="Ben-Gal">{{cite journal|last = Ben-Gal|first = I.|coauthors |author2= Shani A., |author3=Gohr A., |author4=Grau J., |author5=Arviv S., |author6=Shmilovici A., |author7=Posch S. and |author8=Grosse I.|title = Identification of Transcription Factor Binding Sites with Variable-order Bayesian Networks|journal = Bioinformatics|volume = 21|issue = 11|date = 2005|pages = 2657-26662657–2666|url = http://bioinformatics.oxfordjournals.org/cgi/reprint/bti410?ijkey=KkxNhRdTSfvtvXY&keytype=ref|doi = 10.1093/bioinformatics/bti410|pmid = 15797905|doi-access = |url-access = subscription}}</ref><ref name="Grau">{{cite journal|last = Grau|first = J.|coauthorsauthor2 = 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|archive-date = 2018-09-30|access-date = 2007-05-14|archive-url = https://web.archive.org/web/20180930084306/http://www.eng.tau.ac.il/~bengal/VOMBAT.pdf|url-status = dead}}</ref>
These models extend the widely used [[position weight matrix]] (PWM) models, [[Markov model]]s, and Bayesian network (BN) models.
These models extend the widely-used [[position weight matrix]] (PWM) models, [[Markov model]], and [[Bayesian Network]] (BN) models. 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 are also termed 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, ▼
Oregon, USA|pages = 115–123|url = http://www.informatik.unitrier.de/~ley/db/conf/uai/uai1996.html}}</ref>
▲These models extend the widely-used [[position weight matrix]] (PWM) models, [[Markov model]], and [[Bayesian Network]] (BN) models. 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 termedknown as context-specific Bayesian Networksnetworks.<ref name=" Boutilier ">{{cite journal|lastconference |title= Context-specific Boutilier|firstindependence in Bayesian networks |last1= Boutilier |first1=C. | coauthors last2= Friedman |first2=N. , |author-link2=Nir Friedman |last3=Goldszmidt |first3=M. , |last4=Koller |first4=D.| title author-link4= Daphne Context-specificKoller independence|date=1996 in|publisher= Bayesian|book-title= networks| journalpages=115–123 |___location= Reed InCollege, ProceedingsPortland, ofOregon, theUSA |conference=12th Conference on Uncertainty in Artificial Intelligence |date = (August 1–4, 1996 ,) Reed|id= College,|url Portland,= http://www.informatik.uni-trier.de/~ley/db/conf/uai/uai1996.html | arxiv = 1302.3562 }} </ref>
The flexibility in the definition of conditioning subsets of variables turns out to be ofa real advantage forin classification and analysis applications -, as the statistical dependencies between random variables in a sequencessequence of variables (not necessarily adjacent) may be taken into account efficiently, –and in a position-specific and context-specific manner.
==See also==
* [[Variable order Markov models]] ▼* [[Markov chain]]
* [[Examples of Markov chains]]
▲* [[Variable order Markov models]]
* [[Markov process]]
* [[Markov chain Monte Carlo]]
* [[Semi-Markov process]]
* [[BioinformaticsArtificial intelligence]]
* [[Machine learning]]
* [[Artificial Intelligence]]
==References==
<references/>
== External links ==
[[Category:Probability theory]] ▼*VOMBAT: https://www2.informatik.uni-halle.de:8443/VOMBAT/
[[Category:Stochastic processes]]
[[Category:Machine learning]]
[[Category:StatisticsBayesian networks]]
▲[[Category: ProbabilityMarkov theorymodels]]
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