Corpus ID: 6756556

On the Sampling Problem for Kernel Quadrature

@inproceedings{Briol2017OnTS,
  title={On the Sampling Problem for Kernel Quadrature},
  author={François-Xavier Briol and Chris J. Oates and Jon Cockayne and Wilson Ye Chen and Mark A. Girolami},
  booktitle={ICML},
  year={2017}
}
  • François-Xavier Briol, Chris J. Oates, +2 authors Mark A. Girolami
  • Published in ICML 2017
  • Mathematics, Computer Science
  • The standard Kernel Quadrature method for numerical integration with random point sets (also called Bayesian Monte Carlo) is known to converge in root mean square error at a rate determined by the ratio $s/d$, where $s$ and $d$ encode the smoothness and dimension of the integrand. However, an empirical investigation reveals that the rate constant $C$ is highly sensitive to the distribution of the random points. In contrast to standard Monte Carlo integration, for which optimal importance… CONTINUE READING
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