Statistical Aspects of Stochastic Logic Programs

Research output: Other contribution


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 languageEnglish
Number of pages6
Place of PublicationKey West, Florida
Publication statusPublished - 1 Jan 2001

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