Statistical Aspects of Stochastic Logic Programs
Abstract
Stochastic logic programs (SLPs) and the various distributions they define are presented with a stress on their characterisation in terms of Markov chains. Sampling, parameter estimation and structure learning for SLPs are discussed. The application of SLPs to Bayesian learning, computational linguistics and computational biology are considered. Lafferty’s Gibbs-Markov models are compared and contrasted with SLPs.
Cite
Text
Cussens. "Statistical Aspects of Stochastic Logic Programs." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.Markdown
[Cussens. "Statistical Aspects of Stochastic Logic Programs." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.](https://mlanthology.org/aistats/2001/cussens2001aistats-statistical/)BibTeX
@inproceedings{cussens2001aistats-statistical,
title = {{Statistical Aspects of Stochastic Logic Programs}},
author = {Cussens, James},
booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics},
year = {2001},
pages = {77-82},
volume = {R3},
url = {https://mlanthology.org/aistats/2001/cussens2001aistats-statistical/}
}