Editing Dynamic Bayesian network (section)

== History ==
A dynamic Bayesian network (DBN) is often called a "two-timeslice" BN (2TBN) because it says that at any point in time T, the value of a variable can be calculated from the internal regressors and the immediate prior value (time T-1).  DBNs were developed by [[Paul Dagum]] in the early 1990s at [[Stanford University]]'s Section on Medical Informatics.<ref>
{{cite journal
|url=http://research.microsoft.com/en-us/um/people/horvitz/dynamic_network_models_UAI_1992.pdf
|author1=Paul Dagum
|author-link=Paul Dagum
|author2=Adam Galper
|author2-link=Adam Galper
|author3=Eric Horvitz
|author3-link=Eric Horvitz
|title=Dynamic Network Models for Forecasting
|journal = Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence
|date=1992|publisher=AUAI Press|pages=41–48}}
</ref><ref>
{{cite journal
|url=http://research.microsoft.com/en-us/um/people/horvitz/FORECAST.HTM
|author1=Paul Dagum
|author-link=Paul Dagum
|author2=Adam Galper
|author2-link=Adam Galper
|author3=Eric Horvitz
|author3-link=Eric Horvitz
|author4=Adam Seiver
|author4-link=Adam Seiver
|title=Uncertain Reasoning and Forecasting
|journal = International Journal of Forecasting  |volume=11 |issue=1 |date=1995 |pages=73–87 |doi=10.1016/0169-2070(94)02009-e|doi-access=free
}}
</ref> Dagum developed DBNs to unify and extend traditional linear [[State Space Model|state-space models]] such as [[Kalman filter]]s, linear and normal forecasting models such as [[ARMA model|ARMA]] and simple dependency models such as [[hidden Markov model]]s into a general probabilistic representation and inference mechanism for arbitrary nonlinear and non-normal time-dependent domains.<ref>
{{cite journal
|url=https://dslpitt.org/uai/papers/93/p64-dagum.pdf
|archive-url=https://web.archive.org/web/20150907230231/https://dslpitt.org/uai/papers/93/p64-dagum.pdf
|url-status=usurped
|archive-date=September 7, 2015
|author1=Paul Dagum
|author-link=Paul Dagum
|author2=Adam Galper
|author2-link=Adam Galper
|author3=Eric Horvitz
|author3-link=Eric Horvitz
|title=Temporal Probabilistic Reasoning: Dynamic Network Models for Forecasting
|journal = Knowledge Systems Laboratory. Section on Medical Informatics, Stanford University
|date=June 1991}}
</ref><ref>
{{cite journal
|url=http://www-ksl.stanford.edu/KSL_Abstracts/KSL-91-64.html
|author1=Paul Dagum
|author-link=Paul Dagum
|author2=Adam Galper
|author2-link=Adam Galper
|author3=Eric Horvitz
|author3-link=Eric Horvitz
|title=Forecasting Sleep Apnea with Dynamic Network Models
|journal = Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence 
|date=1993|publisher=AUAI Press|pages=64–71}}
</ref>

Today, DBNs are common in [[robotics]], and have shown potential for a wide range of [[data mining]] applications.  For example, they have been used in [[speech recognition]], [[digital forensics]], [[protein]] [[sequencing]], and [[bioinformatics]].  DBN is a generalization of [[hidden Markov models]] and [[Kalman filter]]s.<ref>{{cite book|author1=Stuart Russell|author-link=Stuart J. Russell|author2=Peter Norvig|author2-link=Peter Norvig|title=Artificial Intelligence: A Modern Approach|date=2010|publisher=[[Prentice Hall]]|isbn=978-0136042594|page=566|edition=Third|url=http://51lica.com/wp-content/uploads/2012/05/Artificial-Intelligence-A-Modern-Approach-3rd-Edition.pdf|accessdate=22 October 2014|quote='''dynamic Bayesian networks''' (which include hidden Markov models and Kalman filters as special cases)|url-status=dead|archiveurl=https://web.archive.org/web/20141020191456/http://51lica.com/wp-content/uploads/2012/05/Artificial-Intelligence-A-Modern-Approach-3rd-Edition.pdf|archivedate=20 October 2014}}</ref>

DBNs are conceptually related to probabilistic Boolean networks<ref>
{{cite journal
|pmc=1847796
|author1=Harri Lähdesmäki
|author-link=Harri Lähdesmäki
|author2=Sampsa Hautaniemi
|author2-link=Sampsa Hautaniemi
|author3=Ilya Shmulevich
|author3-link=Ilya Shmulevich
|author4=Olli Yli-Harja
|author4-link=Olli Yli-Harja
|title=Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks
|journal = Signal Processing
|volume=86
|issue=4
|date=2006|pages=814–834|pmid=17415411
|doi=10.1016/j.sigpro.2005.06.008
}}
</ref> and can, similarly, be used to model dynamical systems at steady-state.