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- Q3456604 subject Q8293927.
- Q3456604 abstract "A Dynamic Bayesian Network (DBN) is a Bayesian network which relates variables to each other over adjacent time steps. This 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 when he led research funded by two National Science Foundation grants at Stanford University's Section on Medical Informatics. Dagum developed DBNs to unify and extend traditional linear state-space models such as Kalman filters, linear and normal forecasting models such as ARMA and simple dependency models such as hidden Markov models into a general probabilistic representation and inference mechanism for arbitrary nonlinear and non-normal time-dependent domains.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 filters.".
- Q3456604 wikiPageExternalLink thesis.html.
- Q3456604 wikiPageExternalLink gmtk.
- Q3456604 wikiPageExternalLink libDAI.
- Q3456604 wikiPageExternalLink DBmcmc.
- Q3456604 wikiPageWikiLink Q128570.
- Q3456604 wikiPageWikiLink Q17090669.
- Q3456604 wikiPageWikiLink Q1764062.
- Q3456604 wikiPageWikiLink Q176769.
- Q3456604 wikiPageWikiLink Q189436.
- Q3456604 wikiPageWikiLink Q191307.
- Q3456604 wikiPageWikiLink Q3059012.
- Q3456604 wikiPageWikiLink Q3246940.
- Q3456604 wikiPageWikiLink Q7603.
- Q3456604 wikiPageWikiLink Q8054.
- Q3456604 wikiPageWikiLink Q812540.
- Q3456604 wikiPageWikiLink Q8293927.
- Q3456604 wikiPageWikiLink Q846780.
- Q3456604 wikiPageWikiLink Q924044.
- Q3456604 comment "A Dynamic Bayesian Network (DBN) is a Bayesian network which relates variables to each other over adjacent time steps. This 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).".
- Q3456604 label "Dynamic Bayesian network".