Parameter Estimation in Stochastic Logic Programs (2000)
| Venue: | Machine Learning |
| Citations: | 61 - 3 self |
BibTeX
@INPROCEEDINGS{Cussens00parameterestimation,
author = {James Cussens},
title = {Parameter Estimation in Stochastic Logic Programs},
booktitle = {Machine Learning},
year = {2000},
pages = {2001}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
. Stochastic logic programs (SLPs) are logic programs with labelled clauses which dene a log-linear distribution over refutations of goals. The loglinear distribution provides, by marginalisation, a distribution over variable bindings, allowing SLPs to compactly represent quite complex distributions. We analyse the fundamental statistical properties of SLPs addressing issues concerning innite derivations, `unnormalised' SLPs and impure SLPs. After detailing existing approaches to parameter estimation for log-linear models and their application to SLPs, we present a new algorithm called failure-adjusted maximisation (FAM). FAM is an instance of the EM algorithm that applies specically to normalised SLPs and provides a closed-form for computing parameter updates within an iterative maximisation approach. We empirically show that FAM works on some small examples and discuss methods for applying it to bigger problems. c 2000 Kluwer Academic Publishers. Printed in the Netherlands. ...
