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26

PKDD
2015
Springer

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Estimating Dynamic Graphical Models from Multivariate Time-series Data

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Estimating Dynamic Graphical Models from Multivariate Time-series Data

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We consider the problem of estimating dynamic graphical models that describe the time-evolving conditional dependency structure between a set of data-streams. The bulk of work in such graphical structure learning problems has focused in the stationary i.i.d setting. However, when one introduces dynamics to such models we are forced to make additional assumptions about how the estimated distributions may vary over time. In order to examine the eect of such assumptions we introduce two regularisation schemes that encourage piecewise constant structure within Gaussian graphical models. This article reviews previ

Alexander J. Gibberd, James D. B. Nelson

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Added	16 Apr 2016
Updated	16 Apr 2016
Type	Journal
Year	2015
Where	PKDD
Authors	Alexander J. Gibberd, James D. B. Nelson

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