### Abstract

Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit non-stationary covariance function, but such covariance functions can be difficult to specify and require detailed prior knowledge of the nonstationarity. We propose the Gaussian process product model (GPPM) which models data as the pointwise product of two latent Gaussian processes to nonparametrically infer nonstationary variations of amplitude. This approach differs from other non-parametric approaches to covariance function inference in that it operates on the outputs rather than the inputs, resulting in a significant reduction in computational cost and required data for inference. We present an approximate inference scheme using Expectation Propagation. This variational approximation yields convenient GP hyperparameter selection and compact approximate predictive distributions.

Original language | English (US) |
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Title of host publication | Proceedings of the 25th International Conference on Machine Learning |

Publisher | Association for Computing Machinery (ACM) |

Pages | 1-8 |

Number of pages | 8 |

ISBN (Print) | 9781605582054 |

DOIs | |

State | Published - 2008 |

Externally published | Yes |

Event | 25th International Conference on Machine Learning - Helsinki, Finland Duration: Jul 5 2008 → Jul 9 2008 |

### Publication series

Name | Proceedings of the 25th International Conference on Machine Learning |
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### Other

Other | 25th International Conference on Machine Learning |
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Country | Finland |

City | Helsinki |

Period | 7/5/08 → 7/9/08 |

### All Science Journal Classification (ASJC) codes

- Artificial Intelligence
- Human-Computer Interaction
- Software

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## Cite this

*Proceedings of the 25th International Conference on Machine Learning*(pp. 1-8). (Proceedings of the 25th International Conference on Machine Learning). Association for Computing Machinery (ACM). https://doi.org/10.1145/1390156.1390157