By the same authors

Prolog Issues and Experimental Results of an MCMC Algorithm

Research output: Contribution to conferencePaper

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Prolog Issues and Experimental Results of an MCMC Algorithm. / Angelopoulos, Nicos; Cussens, James.

2001. 186-196.

Research output: Contribution to conferencePaper

Harvard

Angelopoulos, N & Cussens, J 2001, 'Prolog Issues and Experimental Results of an MCMC Algorithm' pp. 186-196. https://doi.org/10.1007/3-540-36524-9_15

APA

Angelopoulos, N., & Cussens, J. (2001). Prolog Issues and Experimental Results of an MCMC Algorithm. 186-196. https://doi.org/10.1007/3-540-36524-9_15

Vancouver

Angelopoulos N, Cussens J. Prolog Issues and Experimental Results of an MCMC Algorithm. 2001. https://doi.org/10.1007/3-540-36524-9_15

Author

Angelopoulos, Nicos ; Cussens, James. / Prolog Issues and Experimental Results of an MCMC Algorithm.

Bibtex - Download

@conference{dc657fa10e914da39fd089d3dbee6406,
title = "Prolog Issues and Experimental Results of an MCMC Algorithm",
abstract = "We present a Markov chain Monte Carlo algorithm that operates on generic model structures that are represented by terms found in the computed answers produced by stochastic logic programs. The objective of this paper is threefold (a) to show that SLD-trees are an elegant means for describing prior distributions over model structures (b) to sketch an implementation of the MCMC algorithm in Prolog, and (c) to provide insights on desirable properties for SLPs.",
author = "Nicos Angelopoulos and James Cussens",
year = "2001",
doi = "10.1007/3-540-36524-9_15",
language = "Undefined/Unknown",
pages = "186--196",

}

RIS (suitable for import to EndNote) - Download

TY - CONF

T1 - Prolog Issues and Experimental Results of an MCMC Algorithm

AU - Angelopoulos, Nicos

AU - Cussens, James

PY - 2001

Y1 - 2001

N2 - We present a Markov chain Monte Carlo algorithm that operates on generic model structures that are represented by terms found in the computed answers produced by stochastic logic programs. The objective of this paper is threefold (a) to show that SLD-trees are an elegant means for describing prior distributions over model structures (b) to sketch an implementation of the MCMC algorithm in Prolog, and (c) to provide insights on desirable properties for SLPs.

AB - We present a Markov chain Monte Carlo algorithm that operates on generic model structures that are represented by terms found in the computed answers produced by stochastic logic programs. The objective of this paper is threefold (a) to show that SLD-trees are an elegant means for describing prior distributions over model structures (b) to sketch an implementation of the MCMC algorithm in Prolog, and (c) to provide insights on desirable properties for SLPs.

U2 - 10.1007/3-540-36524-9_15

DO - 10.1007/3-540-36524-9_15

M3 - Paper

SP - 186

EP - 196

ER -