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A Pragmatic Bayesian Approach to Predictive Uncertainty

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Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment (MLCW 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3944))

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Abstract

We describe an approach to regression based on building a probabilistic model with the aid of visualization. The “stereopsis” data set in the predictive uncertainty challenge is used as a case study, for which we constructed a mixture of neural network experts model. We describe both the ideal Bayesian approach and computational shortcuts required to obtain timely results.

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Authors and Affiliations

  1. Gatsby Computational Neuroscience Unit, University College London, London, WC1N 3AR, UK

    Iain Murray & Edward Snelson

Authors
  1. Iain Murray
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  2. Edward Snelson
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Editors and Affiliations

  1. Max Planck Institute for Biological Cybernetics, Spemannstr. 38, Tübingen, Germany

    Joaquin Quiñonero-Candela

  2. Bar Ilan University, 52900, Ramat Gan, Israel

    Ido Dagan

  3. ITC-IRST, Trento, Italy

    Bernardo Magnini

  4. Université d’Evry-Val d’Essonne, IBISC CNRS FRE 2873 and GENPOLE, 523, Place des terrasses, 91000, Evry, France

    Florence d’Alché-Buc

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© 2006 Springer-Verlag Berlin Heidelberg

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Murray, I., Snelson, E. (2006). A Pragmatic Bayesian Approach to Predictive Uncertainty. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds) Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. MLCW 2005. Lecture Notes in Computer Science(), vol 3944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736790_3

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  • DOI: https://doi.org/10.1007/11736790_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33427-9

  • Online ISBN: 978-3-540-33428-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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