A pragmatic bayesian approach to predictive uncertainty

Authors:
Iain Murray;Edward Snelson
Affiliations:
Gatsby Computational Neuroscience Unit, University College London, London, UK;Gatsby Computational Neuroscience Unit, University College London, London, UK
Venue:
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Year:
2005

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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.