Cussens, James - Department of Computer Science, University of York

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Cussens, James - Department of Computer Science, University of York

Incorporating Linguistics Constraints into Inductive Logic Programming
Extended Stochastic Logic Programs for Informative Priors over C&RTs
Markov Chain Monte Carlo using TreeBased Priors on Model Structure Nicos Angelopoulos James Cussens
Generating Explicit Orderings for Nonmonotonic Logics James Cussens
Leibniz and Boole on logic and probability James Cussens
PartofSpeech Tagging Using Progol James Cussens
CLP(BN ): Constraint Logic Programming for Probabilistic Knowledge Vtor Santos Costa
Estimating Rule Accuracies from Training Data James Cussens \Lambda
Interpretations of Probability, Nonstandard
Individuals, relations and structures in probabilistic models James Cussens
A Bayesian Analysis of Algorithms for Learning Finite Functions James Cussens
Stochastic Logic Programs: Sampling, Inference and Applications James Cussens
Learning Bayesian Networks for Improved Instruction Cache Analysis
Maximum likelihood pedigree reconstruction using integer programming
Integrating by Separating: Combining Probability and Logic with ICL, PRISM and SLPs
Bayesian network learning by compiling to weighted MAX-SAT James Cussens
Morphosyntactic Tagging of Slovene using James Cussens, 1 Saso Dzeroski, 2 Tomaz Erjavec 2
Statistical Aspects of Stochastic Logic Programs James Cussens
Using Maximum Entropy in a Defeasible Logic with Probabilistic Semantics
Tempering for Bayesian C&RT Nicos Angelopoulos nicos@cs.york.ac.uk
GRAMMAR LEARNING USING INDUCTIVE LOGIC PROGRAMMING
Inductive Mercury Programming Barnaby Fisher and James Cussens
Approximate Bayesian computation for the parameters of PRISM programs
On the implementation of MCMC proposals over Stochastic Logic Programs
Using Inductive Logic Programming for Natural Language Processing
Further Inductive Mercury Programming and Barnaby Fisher and James Cussens
A system for `tagging' (possibly ambiguous) words with their correct Partof Speech (POS) is constructed. The system has two components: a lexicon containing
Bayes and PseudoBayes Estimates of Conditional Probabilities and Their Reliability
We consider the theory and practice of using explicit probabilistic bias in Induc tive Logic Programming (ILP). Halpern's logics of probability on the domain and
Exploiting Informative Priors for Bayesian Classification and Regression Trees Nicos Angelopoulos and James Cussens
Introduction Learning Bayesian networks
1 Logic-based Formalisms for Statistical Relational James Cussens
Instruction Cache Prediction Using Bayesian Networks Mark Bartlett and Iain Bate and James Cussens1