Approximate Bayesian computation for the parameters of PRISM programs

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationProceedings of the 20th International Conference on Inductive Logic Programming
Place of PublicationHeidelberg
Number of pages9
ISBN (Electronic)978-3-642-21295-6
ISBN (Print)978-3-642-21294-9
Publication statusPublished - 2011

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