## Abstract

Probabilistic logic programming formalisms permit the definition

of potentially very complex probability distributions. This complexity

can often make learning hard, even when structure is fixed and

learning reduces to parameter estimation. In this paper an approximate

Bayesian computation (ABC) method is presented which computes approximations

to the posterior distribution over PRISM parameters. The

key to ABC approaches is that the likelihood function need not be computed,

instead a ‘distance’ between the observed data and synthetic data

generated by candidate parameter values is used to drive the learning.

This makes ABC highly appropriate for PRISM programs which can have

an intractable likelihood function, but from which synthetic data can be

readily generated. The algorithm is experimentally shown to work well

on an easy problem but further work is required to produce acceptable

results on harder ones.

of potentially very complex probability distributions. This complexity

can often make learning hard, even when structure is fixed and

learning reduces to parameter estimation. In this paper an approximate

Bayesian computation (ABC) method is presented which computes approximations

to the posterior distribution over PRISM parameters. The

key to ABC approaches is that the likelihood function need not be computed,

instead a ‘distance’ between the observed data and synthetic data

generated by candidate parameter values is used to drive the learning.

This makes ABC highly appropriate for PRISM programs which can have

an intractable likelihood function, but from which synthetic data can be

readily generated. The algorithm is experimentally shown to work well

on an easy problem but further work is required to produce acceptable

results on harder ones.

Original language | English |
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Title of host publication | Proceedings of the 20th International Conference on Inductive Logic Programming |

Place of Publication | Heidelberg |

Publisher | Springer |

Pages | 38-46 |

Number of pages | 9 |

Volume | 6489 |

ISBN (Electronic) | 978-3-642-21295-6 |

ISBN (Print) | 978-3-642-21294-9 |

DOIs | |

Publication status | Published - 2011 |