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A pragmatic Bayesian approach to predictive uncertainty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageUndefined/Unknown
Title of host publicationMachine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Textual Entailment.: First PASCAL Machine Learning Challenges Workshop, Southampton, UK, April 11--13, 2005, Revised Selected Papers.
EditorsJoaquin Quiñonero-Candela, Ido Dagan, Bernardo Magnini, Florence D'Alché-Buc
PublisherSpringer-Verlag GmbH
Publication statusPublished - 2006

Publication series

NameLecture Notes in Computer Science
PublisherSpringer

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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