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Active learning of model evidence using Bayesian quadrature

Abstract:

Numerical integration is a key component of many problems in scientific computing, statistical modelling, and machine learning. Bayesian Quadrature is a modelbased method for numerical integration which, relative to standard Monte Carlo methods, offers increased sample efficiency and a more robust estimate of the uncertainty in the estimated integral. We propose a novel Bayesian Quadrature approach for numerical integration when the integrand is non-negative, such as the case of computing the...

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Host title:
Advances in Neural Information Processing Systems
Volume:
1
Pages:
46-54
Publication date:
2012-01-01
ISSN:
1049-5258
ISBN:
9781627480031
Pubs id:
pubs:395743
UUID:
uuid:24618a86-7e60-4bf4-8940-ae02483bed8e
Local pid:
pubs:395743
Source identifiers:
395743
Deposit date:
2013-11-17

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