### Abstract

We present the Gaussian Process Density Sampler (GPDS), an exchangeable generative model for use in nonparametric Bayesian density estimation. Samples drawn from the GPDS are consistent with exact, independent samples from a fixed density function that is a transformation of a function drawn from a Gaussian process prior. Our formulation allows us to infer an unknown density from data using Markov chain Monte Carlo, which gives samples from the posterior distribution over density functions and from the predictive distribution on data space. We can also infer the hyperparameters of the Gaussian process. We compare this density modeling technique to several existing techniques on a toy problem and a skullreconstruction task.

Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference |

Pages | 9-16 |

Number of pages | 8 |

State | Published - Dec 1 2009 |

Externally published | Yes |

Event | 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada Duration: Dec 8 2008 → Dec 11 2008 |

### Publication series

Name | Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference |
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### Other

Other | 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 |
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Country | Canada |

City | Vancouver, BC |

Period | 12/8/08 → 12/11/08 |

### All Science Journal Classification (ASJC) codes

- Information Systems

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## Cite this

*Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference*(pp. 9-16). (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference).