(Enter summary)
Abstract: Bayesian networks are directed acyclic graphs that represent
dependencies between variables in a probabilistic model. Many time series
models, including the hidden Markov models (HMMs) used in speech
recognition and Kalman filter models used in filtering and control applications,
can be viewed as examples of dynamic Bayesian networks. (Update)
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BibTeX entry: (Update)
Z. Ghahramani. Learning dynamic bayesian networks. In C.L. Giles and M. Gori, editors, Adaptive Processing of Temporal Information . Lecture Notes in Artificial Intelligence. SpringerVerlag, 1997. To appear. http://citeseer.ist.psu.edu/ghahramani97learning.html More
@article{ ghahramani98learning,
author = "Zoubin Ghahramani",
title = "Learning Dynamic {Bayesian} Networks",
journal = "Lecture Notes in Computer Science",
volume = "1387",
pages = "168--??",
year = "1998",
url = "citeseer.ist.psu.edu/ghahramani97learning.html" }
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The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.gatsby.ucl.ac.uk/~zoubin/papers.html): More
An Introduction to Variational Methods for Graphical.. - Jordan, Ghahramani.. (1998)
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