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Computing Boundaries in Local Mixture Models

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Geometric Science of Information (GSI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9389))

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Abstract

Local mixture models give an inferentially tractable but still flexible alternative to general mixture models. Their parameter space naturally includes boundaries; near these the behaviour of the likelihood is not standard. This paper shows how convex and differential geometries help in characterising these boundaries. In particular the geometry of polytopes, ruled and developable surfaces is exploited to develop efficient inferential algorithms.

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Author information

Authors and Affiliations

  1. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, N2L 3G1, Canada

    Vahed Maroufy & Paul Marriott

Authors
  1. Vahed Maroufy
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  2. Paul Marriott
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Corresponding author

Correspondence to Vahed Maroufy .

Editor information

Editors and Affiliations

  1. Bâtiment Alan Turing, CS35003, École Polytechnique, Palaiseau, France

    Frank Nielsen

  2. Thales Land\& Air Systems, Limours, France

    Frédéric Barbaresco

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Maroufy, V., Marriott, P. (2015). Computing Boundaries in Local Mixture Models. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2015. Lecture Notes in Computer Science(), vol 9389. Springer, Cham. https://doi.org/10.1007/978-3-319-25040-3_62

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  • DOI: https://doi.org/10.1007/978-3-319-25040-3_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25039-7

  • Online ISBN: 978-3-319-25040-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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