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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications
verfasst von : Florian Lemmerich, Philipp Singer, Martin Becker, Lisette Espin-Noboa, Dimitar Dimitrov, Denis Helic, Andreas Hotho, Markus Strohmaier
Erschienen in: Machine Learning and Knowledge Discovery in Databases
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Abstract
Sequential data can be found in many settings, e.g., as sequences of visited websites or as location sequences of travellers. To improve the understanding of the underlying mechanisms that generate such sequences, the HypTrails approach provides for a novel data analysis method. Based on first-order Markov chain models and Bayesian hypothesis testing, it allows for comparing a set of hypotheses, i.e., beliefs about transitions between states, with respect to their plausibility considering observed data. HypTrails has been successfully employed to study phenomena in the online and the offline world. In this talk, we want to give an introduction to HypTrails and showcase selected real-world applications on urban mobility and reading behavior on Wikipedia.