3 accepted papers
To learn a Bayesian network structure from data, one popular approach is to maximize a decomposable likelihood-based score. While various scores have been proposed, they usually assume a uniform prior, or “penalty,” over the possible directed acyclic graphs (DAGs); relatively little attention has be…
To compare learning algorithms that differ by the adopted statistical paradigm, model class, or search heuristic, it is common to evaluate the performance on training data of varying size. Measuring the performance is straightforward if the data are generated from a known model, the ground truth. Ho…
Context trees (CT) are a widely used tool in machine learning for representing context-specific independences in conditional probability distributions. Parsimonious context trees (PCTs) are a recently proposed generalization of CTs that can enable statistically more efficient learning due to a highe…