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. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Murray, I., & Snelson, E. (2006). A pragmatic Bayesian approach to predictive uncertainty. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3944 LNAI, pp. 33–40). Springer Verlag. https://doi.org/10.1007/11736790_3
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