99 citations · 192 across the 4 of their papers we have counts for
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Loglinear models for first-order probabilistic reasoning
James Cussens
Recent work on loglinear models in probabilistic constraint logic programming is applied to first-order probabilistic reasoning. Probabilities are defined directly on the proofs of…
Stochastic Logic Programs: Sampling, Inference and Applications
James Cussens
Algorithms for exact and approximate inference in stochastic logic programs (SLPs) are presented, based respectively, on variable elimination and importance sampling. We then show…
Markov Chain Monte Carlo using Tree-Based Priors on Model Structure
Nicos Angelopoulos, James Cussens
We present a general framework for defining priors on model structure and sampling from the posterior using the Metropolis-Hastings algorithm. The key idea is that structure priors…
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge
Vitor Santos Costa, David Page, Maleeha Qazi +1
We present CLP(BN), a novel approach that aims at expressing Bayesian networks through the constraint logic programming framework. Arguably, an important limitation of traditional…