A pragmatic Bayesian approach to predictive uncertainty

Iain Murray; Edward Snelson

A pragmatic Bayesian approach to predictive uncertainty

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	Undefined/Unknown
Title of host publication	Machine 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.
Editors	Joaquin Quiñonero-Candela, Ido Dagan, Bernardo Magnini, Florence D'Alché-Buc
Publisher	Springer-Verlag GmbH
Publication status	Published - 2006

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer

Access to Document

http://link.springer.com/chapter/10.1007%2F11736790_3

Cite this

Murray, I., & Snelson, E. (2006). A pragmatic Bayesian approach to predictive uncertainty. In J. Quiñonero-Candela, I. Dagan, B. Magnini, & F. D'Alché-Buc (Eds.), Machine 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. (Lecture Notes in Computer Science). Springer-Verlag GmbH. http://link.springer.com/chapter/10.1007%2F11736790_3

Murray, Iain ; Snelson, Edward. / A pragmatic Bayesian approach to predictive uncertainty. Machine 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.. editor / Joaquin Quiñonero-Candela ; Ido Dagan ; Bernardo Magnini ; Florence D'Alché-Buc. Springer-Verlag GmbH, 2006. (Lecture Notes in Computer Science).

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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.",

author = "Iain Murray and Edward Snelson",

year = "2006",

language = "Undefined/Unknown",

series = "Lecture Notes in Computer Science",

publisher = "Springer-Verlag GmbH",

editor = "Joaquin Qui{\~n}onero-Candela and Ido Dagan and Bernardo Magnini and Florence D'Alch{\'e}-Buc",

booktitle = "Machine 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.",

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Murray, I & Snelson, E 2006, A pragmatic Bayesian approach to predictive uncertainty. in J Quiñonero-Candela, I Dagan, B Magnini & F D'Alché-Buc (eds), Machine 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.. Lecture Notes in Computer Science, Springer-Verlag GmbH. <http://link.springer.com/chapter/10.1007%2F11736790_3>

A pragmatic Bayesian approach to predictive uncertainty. / Murray, Iain; Snelson, Edward.

Machine 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.. ed. / Joaquin Quiñonero-Candela; Ido Dagan; Bernardo Magnini; Florence D'Alché-Buc. Springer-Verlag GmbH, 2006. (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Murray, Iain

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PY - 2006

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AB - 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.

M3 - Conference contribution

T3 - Lecture Notes in Computer Science

BT - Machine 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.

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A2 - Magnini, Bernardo

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Murray I, Snelson E. A pragmatic Bayesian approach to predictive uncertainty. In Quiñonero-Candela J, Dagan I, Magnini B, D'Alché-Buc F, editors, Machine 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.. Springer-Verlag GmbH. 2006. (Lecture Notes in Computer Science).