## Abstract

We define a probability distribution over equivalence classes of binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features. We identify a simple generative process that results in the same distribution over equivalence classes, which we call the Indian buffet process. We illustrate the use of this distribution as a prior in an infinite latent feature model, deriving a Markov chain Monte Carlo algorithm for inference in this model and applying the algorithm to an image dataset.

Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference |

Pages | 475-482 |

Number of pages | 8 |

State | Published - Dec 1 2005 |

Externally published | Yes |

Event | 2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada Duration: Dec 5 2005 → Dec 8 2005 |

### Other

Other | 2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 |
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Country | Canada |

City | Vancouver, BC |

Period | 12/5/05 → 12/8/05 |

## All Science Journal Classification (ASJC) codes

- Computer Networks and Communications
- Information Systems
- Signal Processing