A Statistical Model for Event Sequence Data

Kevin Heins; Hal Stern

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.

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@InProceedings{pmlr-v33-heins14, title = {{A Statistical Model for Event Sequence Data}}, author = {Kevin Heins and Hal Stern}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {338--346}, year = {2014}, editor = {Samuel Kaski and Jukka Corander}, 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 = {http://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.} }

%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 %J Proceedings of Machine Learning Research %P 338--346 %U http://proceedings.mlr.press %V 33 %W PMLR %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.

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 PY - 2014/04/02 DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-heins14 PB - PMLR SP - 338 DP - PMLR EP - 346 L1 - http://proceedings.mlr.press/v33/heins14.pdf UR - http://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 -