the archive

#structure learning

4 results

cs.SI2019

Learning Graphs from Noisy Epidemic Cascades

Jessica Hoffmann, Constantine Caramanis

The paper develops polynomial‑time algorithms to infer the weighted edges and structure of graphs from noisy epidemic cascade data, handling both limited‑noise (noisy infection tim…

#graph inference#epidemic cascades#noisy observations#structure learning

cs.LG2019

Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain

Ran Wang, Suhe Ye, Ke Li +1

The paper introduces a Bayesian network approach to discover label correlations and determine the label order for multi-label classifier chains, using conditional entropy and a heu…

#multi-label learning#classifier chains#bayesian networks#label correlation

math.ST2019

Learning directed acyclic graphs based on sparsest permutations

Garvesh Raskutti, Caroline Uhler

The paper introduces the sparsest permutation (SP) algorithm for learning Bayesian network structures, proving its consistency under weaker conditions than the faithfulness assumpt…

#bayesian networks#directed acyclic graphs#causal inference#structure learning

math.ST2019

Bayesian Structure Learning in Graphical Models using Shrinkage priors

Sayantan Banerjee

The paper proposes a Bayesian method for learning the sparsity pattern of high‑dimensional precision matrices using a Dirichlet‑Laplace shrinkage prior and provides a Gibbs samplin…

#bayesian inference#graphical models#structure learning#shrinkage priors