Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvant...
A wide variety of Dirichlet-multinomial ‘topic’ models have found interesting applications in recent years. While Gibbs sampling remains an important method of inference in su...
Hierarchical probabilistic modeling of discrete data has emerged as a powerful tool for text analysis. Posterior inference in such models is intractable, and practitioners rely on...
The article contributes a derivation of variational Bayes for a large class of topic models by generalising from the well-known model of latent Dirichcation. For an abstraction of ...
Co-clustering has emerged as an important technique for mining contingency data matrices. However, almost all existing coclustering algorithms are hard partitioning, assigning each...