Bayesian & Probabilistic

A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior JMLR 2005 Assessing Approximate Inference for Binary Gaussian Process Classification JMLR 2005 Clustering on the Unit Hypersphere using von Mises-Fisher Distributions JMLR 2005 Convergence Theorems for Generalized Alternating Minimization Procedures JMLR 2005 Estimating Functions for Blind Separation When Sources Have Variance Dependencies JMLR 2005 Estimation of Non-Normalized Statistical Models by Score Matching JMLR 2005 Expectation Consistent Approximate Inference JMLR 2005 Learning Hidden Variable Networks: The Information Bottleneck Approach JMLR 2005 Learning Module Networks JMLR 2005 Local Propagation in Conditional Gaussian Bayesian Networks JMLR 2005 Exact Bayesian Structure Discovery in Bayesian Networks JMLR 2004 Lossless Online Bayesian Bagging JMLR 2004 PAC-learnability of Probabilistic Deterministic Finite State Automata JMLR 2004 The Minimum Error Minimax Probability Machine JMLR 2004 A Generative Model for Separating Illumination and Reflectance from Images JMLR 2003 A Maximum Likelihood Approach to Single-channel Source Separation JMLR 2003 A Multiscale Framework For Blind Separation of Linearly Mixed Signals JMLR 2003 An Approximate Analytical Approach to Resampling Averages JMLR 2003 A Unified Framework for Model-based Clustering JMLR 2003 Beyond Independent Components: Trees and Clusters JMLR 2003 Blind Source Recovery: A Framework in the State Space JMLR 2003 Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains JMLR 2003 Latent Dirichlet Allocation JMLR 2003 Learning Probabilistic Models: An Expected Utility Maximization Approach JMLR 2003 On Inclusion-Driven Learning of Bayesian Networks JMLR 2003