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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

LanguageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Proceedings
PublisherSpringer Verlag Wien
Pages354-357
Number of pages4
Volume10536 LNAI
ISBN (Print)9783319712727
DOIs
StatusPublished - 2017
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017 - Skopje, Macedonia, The Former Yugoslav Republic of
Duration: 18 Sep 201722 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10536 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017
CountryMacedonia, The Former Yugoslav Republic of
CitySkopje
Period18/09/1722/09/17

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)

Cite this

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 (Vol. 10536 LNAI, pp. 354-357). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10536 LNAI). Springer Verlag Wien. DOI: 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. Vol. 10536 LNAI Springer Verlag Wien, 2017. p. 354-357 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10536 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. vol. 10536 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10536 LNAI, Springer Verlag Wien, pp. 354-357, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2017, Skopje, Macedonia, The Former Yugoslav Republic of, 18/09/17. DOI: 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. Vol. 10536 LNAI. Springer Verlag Wien. 2017. p. 354-357. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Available from, DOI: 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. Vol. 10536 LNAI Springer Verlag Wien, 2017. pp. 354-357 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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