Statistical Aspects of Stochastic Logic Programs

James Cussens

Statistical Aspects of Stochastic Logic Programs

James Cussens

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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

Original language	English
Title of host publication	Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001)
Subtitle of host publication	PMLR
Pages	77-82
Volume	R3
Publication status	Published - 4 Jan 2001

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@inproceedings{af7906bf53624a868991cbc4d01f0b66,

title = "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{\textquoteright}s Gibbs-Markov models are compared and contrasted with SLPs",

author = "James Cussens",

year = "2001",

month = jan,

day = "4",

language = "English",

volume = "R3",

pages = "77--82",

booktitle = "Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001)",

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T1 - Statistical Aspects of Stochastic Logic Programs

AU - Cussens, James

PY - 2001/1/4

Y1 - 2001/1/4

N2 - 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

AB - 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

M3 - Conference Contribution (Conference Proceeding)

VL - R3

SP - 77

EP - 82

BT - Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS 2001)

ER -

Statistical Aspects of Stochastic Logic Programs

Abstract

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