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Linear Belief Functions for Data Analytics

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Belief Functions: Theory and Applications (BELIEF 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11069))

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Abstract

This paper studies the application of linear belief functions to both classic and Bayesian statistic data analysis. In particular, it explores how to combine direct observations with/without distributional assumptions as linear belief functions for estimating population mean, how to combine system equations and measurement equations with direct observations in time series models and Bayesian linear regressions. It illustrates the use of Linear Model Operating Systems (LMOS).

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References

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Author information

Authors and Affiliations

  1. The University of Akron, Akron, USA

    Liping Liu

Authors
  1. Liping Liu
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Correspondence to Liping Liu .

Editor information

Editors and Affiliations

  1. University of Technology of Compiègne, Compiègne Cedex, France

    Sébastien Destercke

  2. UMR CNRS 7253 Heudiasyc, Université de Technologie de Compiègne, Compiègne Cedex, France

    Thierry Denoeux

  3. Department of Computing and Communication, Oxford Brookes University, Oxford, United Kingdom

    Fabio Cuzzolin

  4. Université de Rennes 1, Lannion Cedex, France

    Arnaud Martin

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Liu, L. (2018). Linear Belief Functions for Data Analytics. In: Destercke, S., Denoeux, T., Cuzzolin, F., Martin, A. (eds) Belief Functions: Theory and Applications. BELIEF 2018. Lecture Notes in Computer Science(), vol 11069. Springer, Cham. https://doi.org/10.1007/978-3-319-99383-6_23

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  • DOI: https://doi.org/10.1007/978-3-319-99383-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99382-9

  • Online ISBN: 978-3-319-99383-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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