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- W13278560 abstract "AbstractStandard 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.KeywordsHide Markov ModelPosterior DistributionTime StampDynamic Time WarpingInference AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
- W13278560 created "2016-06-24" @default.
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- W13278560 date "2012-01-01" @default.
- W13278560 modified "2023-09-26" @default.
- W13278560 title "Uncertain Observation Times" @default.
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- W13278560 doi "https://doi.org/10.1007/978-3-642-33362-0_30" @default.
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