Skip to main content

A Pragmatic Bayesian Approach to Predictive Uncertainty

  • Conference paper
  • 1895 Accesses

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

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.

Keywords

  • Markov Chain Monte Carlo
  • Loss Function
  • Input Space
  • Predictive Distribution
  • Markov Chain Monte Carlo Method

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/11736790_3
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-540-33428-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Sinz, F.H., Candela, J.Q., Bakır, G.H., Rasmussen, C.E., Franz, M.O.: Learning depth from stereo. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds.) DAGM 2004. LNCS, vol. 3175, pp. 245–252. Springer, Heidelberg (2004)

    CrossRef  Google Scholar 

  2. Jordan, M.I., Jacobs, R.A.: Hierarchical mixtures of experts and the EM algorithm. Neural Computation 6, 181–214 (1994)

    CrossRef  Google Scholar 

  3. Neal, R.M.: Bayesian Learning for Neural Networks. Lecture Notes in Statistics, vol. 118. Springer, New York (1996)

    MATH  Google Scholar 

  4. Neal, R.M.: Flexible Bayesian modeling software (FBM)(2003), Available through http://www.cs.toronto.edu/~radford/

  5. Neal, R.M.: Probabilistic inference using Markov chain Monte Carlo methods. Technical report. Dept. of Computer Science, University of Toronto (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • 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)