Statistical Aspects of Stochastic Logic Programs

James Cussens
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:77-82, 2001.

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 this Paper


BibTeX
@InProceedings{pmlr-vR3-cussens01a, title = {Statistical Aspects of Stochastic Logic Programs}, author = {Cussens, James}, booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics}, pages = {77--82}, year = {2001}, editor = {Richardson, Thomas S. and Jaakkola, Tommi S.}, volume = {R3}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r3/cussens01a/cussens01a.pdf}, url = {http://proceedings.mlr.press/r3/cussens01a.html}, 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.}, note = {Reissued by PMLR on 31 March 2021.} }
Endnote
%0 Conference Paper %T Statistical Aspects of Stochastic Logic Programs %A James Cussens %B Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2001 %E Thomas S. Richardson %E Tommi S. Jaakkola %F pmlr-vR3-cussens01a %I PMLR %P 77--82 %U http://proceedings.mlr.press/r3/cussens01a.html %V R3 %X 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. %Z Reissued by PMLR on 31 March 2021.
APA
Cussens, J.. (2001). Statistical Aspects of Stochastic Logic Programs. Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R3:77-82 Available from http://proceedings.mlr.press/r3/cussens01a.html. Reissued by PMLR on 31 March 2021.

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