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.
|Publisher||MORGAN KAUFMANN PUB INC|
|Number of pages||6|
|Place of Publication||Key West, Florida|
|Publication status||Published - 1 Jan 2001|