Abstract
Standard temporal models assume that observation times are correct, whereas in many real-world settings (particularly those involving human data entry) noisy time stamps are quite common. Serious problems arise when these time stamps are taken literally. This paper introduces a modeling framework for handling uncertainty in observation times and describes inference algorithms that, under certain reasonable assumptions about the nature of time-stamp errors, have linear time complexity.
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Chatterjee, S., Russell, S. (2012). Uncertain Observation Times. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds) Scalable Uncertainty Management. SUM 2012. Lecture Notes in Computer Science(), vol 7520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33362-0_30
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DOI: https://doi.org/10.1007/978-3-642-33362-0_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33361-3
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