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.
@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 -
Heins, K. & Stern, H.. (2014). A Statistical Model for Event Sequence Data. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in PMLR 33:338-346
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