Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications

Florian Lemmerich, Philipp Singer, Martin Becker, Lisette Espin-Noboa, Dimitar Dimitrov, Denis Helic, Andreas Hotho, Markus Strohmaier

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

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.

Originalspracheenglisch
TitelMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings
Herausgeber (Verlag)Springer Verlag Wien
Seiten354-357
Seitenumfang4
Band10536 LNAI
ISBN (Print)9783319712727
DOIs
PublikationsstatusVeröffentlicht - 2017
VeranstaltungEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 - Skopje, Mazedonien, ehemalige jugoslawische Republik
Dauer: 18 Sep 201722 Sep 2017

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band10536 LNAI
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

KonferenzEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017
LandMazedonien, ehemalige jugoslawische Republik
OrtSkopje
Zeitraum18/09/1722/09/17

Fingerprint

Bayesian Approach
Markov processes
Websites
Testing
Markov Chain Model
Wikipedia
Hypothesis Testing
Real-world Applications
Data analysis
First-order

ASJC Scopus subject areas

  • !!Theoretical Computer Science
  • !!Computer Science(all)

Dies zitieren

Lemmerich, F., Singer, P., Becker, M., Espin-Noboa, L., Dimitrov, D., Helic, D., ... Strohmaier, M. (2017). Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications. in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings (Band 10536 LNAI, S. 354-357). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10536 LNAI). Springer Verlag Wien. https://doi.org/10.1007/978-3-319-71273-4_30

Comparing Hypotheses About Sequential Data : A Bayesian Approach and Its Applications. / Lemmerich, Florian; Singer, Philipp; Becker, Martin; Espin-Noboa, Lisette; Dimitrov, Dimitar; Helic, Denis; Hotho, Andreas; Strohmaier, Markus.

Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Band 10536 LNAI Springer Verlag Wien, 2017. S. 354-357 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10536 LNAI).

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Lemmerich, F, Singer, P, Becker, M, Espin-Noboa, L, Dimitrov, D, Helic, D, Hotho, A & Strohmaier, M 2017, Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications. in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Bd. 10536 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 10536 LNAI, Springer Verlag Wien, S. 354-357, Skopje, Mazedonien, ehemalige jugoslawische Republik, 18/09/17. https://doi.org/10.1007/978-3-319-71273-4_30
Lemmerich F, Singer P, Becker M, Espin-Noboa L, Dimitrov D, Helic D et al. Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications. in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Band 10536 LNAI. Springer Verlag Wien. 2017. S. 354-357. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-71273-4_30
Lemmerich, Florian ; Singer, Philipp ; Becker, Martin ; Espin-Noboa, Lisette ; Dimitrov, Dimitar ; Helic, Denis ; Hotho, Andreas ; Strohmaier, Markus. / Comparing Hypotheses About Sequential Data : A Bayesian Approach and Its Applications. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings. Band 10536 LNAI Springer Verlag Wien, 2017. S. 354-357 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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