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ICML
2007
IEEE

Modeling changing dependency structure in multivariate time series

10 years 2 months ago
Modeling changing dependency structure in multivariate time series
We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vector-valued observations using undirected Gaussian graphical models, whose structure we estimate. We show how we can exactly compute the MAP segmentation, as well as how to draw perfect samples from the posterior over segmentations, simultaneously accounting for uncertainty about the number and location of changepoints, as well as uncertainty about the covariance structure. We illustrate the technique by applying it to financial data and to bee tracking data.
Xiang Xuan, Kevin P. Murphy
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Xiang Xuan, Kevin P. Murphy
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