Tractable Bayesian Inference of Time-Series Dependence Structure

Michael Siracusa, John Fisher III
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, PMLR 5:528-535, 2009.

Abstract

We consider the problem of Bayesian inference over graphical structures describing the interactions among multiple vector time-series. A directed temporal interaction model is presented which assumes a fixed dependence structure among time-series. Using a conjugate prior over this model’s structure and parameters, we focus our attention on characterizing the exact posterior uncertainty in the structure given data. The model is extended via the introduction of a dynamically evolving latent variable which indexes dependence structures over time. Performing inference using this model yields promising results when analyzing the interaction of multiple tracked moving objects.

Cite this Paper

BibTeX
@InProceedings{pmlr-v5-siracusa09a, title = {Tractable Bayesian Inference of Time-Series Dependence Structure}, author = {Siracusa, Michael and III, John Fisher}, booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics}, pages = {528--535}, year = {2009}, editor = {van Dyk, David and Welling, Max}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/siracusa09a/siracusa09a.pdf}, url = {https://proceedings.mlr.press/v5/siracusa09a.html}, abstract = {We consider the problem of Bayesian inference over graphical structures describing the interactions among multiple vector time-series. A directed temporal interaction model is presented which assumes a fixed dependence structure among time-series. Using a conjugate prior over this model’s structure and parameters, we focus our attention on characterizing the exact posterior uncertainty in the structure given data. The model is extended via the introduction of a dynamically evolving latent variable which indexes dependence structures over time. Performing inference using this model yields promising results when analyzing the interaction of multiple tracked moving objects.} }
Endnote
%0 Conference Paper %T Tractable Bayesian Inference of Time-Series Dependence Structure %A Michael Siracusa %A John Fisher III %B Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-siracusa09a %I PMLR %P 528--535 %U https://proceedings.mlr.press/v5/siracusa09a.html %V 5 %X We consider the problem of Bayesian inference over graphical structures describing the interactions among multiple vector time-series. A directed temporal interaction model is presented which assumes a fixed dependence structure among time-series. Using a conjugate prior over this model’s structure and parameters, we focus our attention on characterizing the exact posterior uncertainty in the structure given data. The model is extended via the introduction of a dynamically evolving latent variable which indexes dependence structures over time. Performing inference using this model yields promising results when analyzing the interaction of multiple tracked moving objects.
RIS
TY - CPAPER TI - Tractable Bayesian Inference of Time-Series Dependence Structure AU - Michael Siracusa AU - John Fisher III BT - Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-siracusa09a PB - PMLR DP - Proceedings of Machine Learning Research VL - 5 SP - 528 EP - 535 L1 - http://proceedings.mlr.press/v5/siracusa09a/siracusa09a.pdf UR - https://proceedings.mlr.press/v5/siracusa09a.html AB - We consider the problem of Bayesian inference over graphical structures describing the interactions among multiple vector time-series. A directed temporal interaction model is presented which assumes a fixed dependence structure among time-series. Using a conjugate prior over this model’s structure and parameters, we focus our attention on characterizing the exact posterior uncertainty in the structure given data. The model is extended via the introduction of a dynamically evolving latent variable which indexes dependence structures over time. Performing inference using this model yields promising results when analyzing the interaction of multiple tracked moving objects. ER -
APA
Siracusa, M. & III, J.F.. (2009). Tractable Bayesian Inference of Time-Series Dependence Structure. Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 5:528-535 Available from https://proceedings.mlr.press/v5/siracusa09a.html.

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