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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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)CrossRefGoogle Scholar
  2. 2.
    Jordan, M.I., Jacobs, R.A.: Hierarchical mixtures of experts and the EM algorithm. Neural Computation 6, 181–214 (1994)CrossRefGoogle Scholar
  3. 3.
    Neal, R.M.: Bayesian Learning for Neural Networks. Lecture Notes in Statistics, vol. 118. Springer, New York (1996)zbMATHGoogle Scholar
  4. 4.
    Neal, R.M.: Flexible Bayesian modeling software (FBM)(2003), Available through
  5. 5.
    Neal, R.M.: Probabilistic inference using Markov chain Monte Carlo methods. Technical report. Dept. of Computer Science, University of Toronto (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Iain Murray
    • 1
  • Edward Snelson
    • 1
  1. 1.Gatsby Computational Neuroscience UnitUniversity College LondonLondonUK

Personalised recommendations