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Learning Dynamic Bayesian Networks (1997)  (Make Corrections)  (39 citations)
Zoubin Ghahramani Department of Computer Science University of Toronto...
Lecture Notes in Computer Science



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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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12:   A unifying review of linear Gaussian models - Roweis, Ghahramani - 1997
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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)   (Correct)
Variational Learning for Switching State-Space Models - Ghahramani, Hinton (2000)   (Correct)
A Unifying Review of Linear Gaussian Models - Roweis, Ghahramani (1997)   (Correct)

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