Cussens, J. (2001) Parameter estimation in stochastic logic programs. Machine Learning, 44 (3). pp. 245-271. ISSN 0885-6125Full text not available from this repository.
Stochastic logic programs (SLPs) are logic programs with parameterised clauses which define a log-linear distribution over refutations of goals. The log-linear distribution provides, by marginalisation, a distribution over variable bindings, allowing SLPs to compactly represent quite complex distributions. We analyse the fundamental statistical properties of SLPs addressing issues concerning infinite derivations, 'unnormalised SLPs and impure SLPs. After detailing existing approaches to parameter estimation for log-linear models and their application to SLPs, we present a new algorithm called failure-adjusted maximisation (FAM). FAM is an instance of the EM algorithm that applies specifically to normalised SLPs and provides a closed-form for computing parameter updates within an iterative maximisation approach. We empirically show that FAM works on some small examples and discuss methods for applying it to bigger problems.
|Academic Units:||The University of York > Computer Science (York)|
|Depositing User:||York RAE Import|
|Date Deposited:||30 Mar 2009 08:36|
|Last Modified:||30 Mar 2009 08:36|
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