### Abstract

Markov chain Monte Carlo (MCMC) is a popular and successful general-purpose tool for Bayesian inference. However, MCMC cannot be practically applied to large data sets because of the prohibitive cost of evaluating every likelihood term at every iteration. Here we present Firefly Monte Carlo (FlyMC) an auxiliary variable MCMC algorithm that only queries the likelihoods of a potentially small subset of the data at each iteration yet simulates from the exact posterior distribution, in contrast to recent proposals that are approximate even in the asymptotic limit. FlyMC is compatible with a wide variety of modern MCMC algorithms, and only requires a lower bound on the per-datum likelihood factors. In experiments, we find that FlyMC generates samples from the posterior more than an order of magnitude faster than regular MCMC, opening up MCMC methods to larger datasets than were previously considered feasible.

Original language | English (US) |
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Title of host publication | Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014 |

Editors | Nevin L. Zhang, Jin Tian |

Publisher | AUAI Press |

Pages | 543-552 |

Number of pages | 10 |

ISBN (Electronic) | 9780974903910 |

State | Published - Jan 1 2014 |

Externally published | Yes |

Event | 30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 - Quebec City, Canada Duration: Jul 23 2014 → Jul 27 2014 |

### Publication series

Name | Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014 |
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### Other

Other | 30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 |
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Country | Canada |

City | Quebec City |

Period | 7/23/14 → 7/27/14 |

### All Science Journal Classification (ASJC) codes

- Artificial Intelligence

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## Cite this

*Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014*(pp. 543-552). (Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014). AUAI Press.