We present a method for distributing collapsed Gibbs sampling over multiple processors that is simple, statistically correct, and memory efficient. The method uses blocked sampling, dividing the training data into relatively large sized blocks, and distributing the sampling of each block over multiple processors. At the end of each parallel run, MetropolisHastings rejection sampling is performe...

Learning a tree substitution grammar is very challenging due to derivational ambiguity. Our recent approach used a Bayesian non-parametric model to induce good derivations from treebanked input (Cohn et al., 2009), biasing towards small grammars composed of small generalisable productions. In this paper we present a novel training method for the model using a blocked Metropolis-Hastings sampler...

Boolean satisfiability (SAT) has become a core technology in many application domains, such as planning and formal verification, and continues to find various new application domains today. The SAT-based approach divides into three steps: encoding, preprocessing, and search. It is often argued that by encoding arbitrary Boolean formulas in conjunctive normal form (CNF), structural properties of...