167 citations · 522 across the 16 of their papers we have counts for
4 papers · 1 filter
Advances in Bayesian Network Learning using Integer Programming
Mark Bartlett, James Cussens
We consider the problem of learning Bayesian networks (BNs) from complete discrete data. This problem of discrete optimisation is formulated as an integer program (IP). We describe…
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…