A Statistical Model for Event Sequence Data

Kevin Heins, Hal Stern
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:338-346, 2014.

Abstract

The identification of recurring patterns within a sequence of events is an important task in behavior research. In this paper, we consider a general probabilistic framework for identifying such patterns, by distinguishing between events that belong to a pattern and events that occur as part of background processes. The event processes, both for background events and events that are part of recurring patterns, are modeled as competing renewal processes. Using this framework, we develop an inference procedure to detect the sequences present in observed data. Our method is compared to a current approach used within the ethology literature on both simulated data and data collected to study the impact of fragmented and unpredictable maternal behavior on cognitive development of children.

Cite this Paper

BibTeX
@InProceedings{pmlr-v33-heins14, title = {{A Statistical Model for Event Sequence Data}}, author = {Heins, Kevin and Stern, Hal}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {338--346}, year = {2014}, editor = {Kaski, Samuel and Corander, Jukka}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/heins14.pdf}, url = {https://proceedings.mlr.press/v33/heins14.html}, abstract = {The identification of recurring patterns within a sequence of events is an important task in behavior research. In this paper, we consider a general probabilistic framework for identifying such patterns, by distinguishing between events that belong to a pattern and events that occur as part of background processes. The event processes, both for background events and events that are part of recurring patterns, are modeled as competing renewal processes. Using this framework, we develop an inference procedure to detect the sequences present in observed data. Our method is compared to a current approach used within the ethology literature on both simulated data and data collected to study the impact of fragmented and unpredictable maternal behavior on cognitive development of children.} }
Endnote
%0 Conference Paper %T A Statistical Model for Event Sequence Data %A Kevin Heins %A Hal Stern %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-heins14 %I PMLR %P 338--346 %U https://proceedings.mlr.press/v33/heins14.html %V 33 %X The identification of recurring patterns within a sequence of events is an important task in behavior research. In this paper, we consider a general probabilistic framework for identifying such patterns, by distinguishing between events that belong to a pattern and events that occur as part of background processes. The event processes, both for background events and events that are part of recurring patterns, are modeled as competing renewal processes. Using this framework, we develop an inference procedure to detect the sequences present in observed data. Our method is compared to a current approach used within the ethology literature on both simulated data and data collected to study the impact of fragmented and unpredictable maternal behavior on cognitive development of children.
RIS
TY - CPAPER TI - A Statistical Model for Event Sequence Data AU - Kevin Heins AU - Hal Stern BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-heins14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 33 SP - 338 EP - 346 L1 - http://proceedings.mlr.press/v33/heins14.pdf UR - https://proceedings.mlr.press/v33/heins14.html AB - The identification of recurring patterns within a sequence of events is an important task in behavior research. In this paper, we consider a general probabilistic framework for identifying such patterns, by distinguishing between events that belong to a pattern and events that occur as part of background processes. The event processes, both for background events and events that are part of recurring patterns, are modeled as competing renewal processes. Using this framework, we develop an inference procedure to detect the sequences present in observed data. Our method is compared to a current approach used within the ethology literature on both simulated data and data collected to study the impact of fragmented and unpredictable maternal behavior on cognitive development of children. ER -
APA
Heins, K. & Stern, H.. (2014). A Statistical Model for Event Sequence Data. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 33:338-346 Available from https://proceedings.mlr.press/v33/heins14.html.

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