Bayesian & Probabilistic

Learning GP-BayesFilters via Gaussian process latent variable models RSS 2009 Learning in Markov Random Fields using Tempered Transitions NeurIPS 2009 Learning models of object structure NeurIPS 2009 Learning with Compressible Priors NeurIPS 2009 Linearly constrained Bayesian matrix factorization for blind source separation NeurIPS 2009 Localizing Bugs in Program Executions with Graphical Models NeurIPS 2009 Local Rules for Global MAP: When Do They Work ? NeurIPS 2009 Marginal Likelihood Integrals for Mixtures of Independence Models JMLR 2009 Measuring model complexity with the prior predictive NeurIPS 2009 Modeling the spacing effect in sequential category learning NeurIPS 2009 Modelling Relational Data using Bayesian Clustered Tensor Factorization NeurIPS 2009 Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling NeurIPS 2009 Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations NeurIPS 2009 Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution NeurIPS 2009 Nonparametric Bayesian Texture Learning and Synthesis NeurIPS 2009 Nonparametric Latent Feature Models for Link Prediction NeurIPS 2009 Non-stationary continuous dynamic Bayesian networks NeurIPS 2009 Particle-based Variational Inference for Continuous Systems NeurIPS 2009 Perceptual Multistability as Markov Chain Monte Carlo Inference NeurIPS 2009 Perturbation Corrections in Approximate Inference: Mixture Modelling Applications JMLR 2009 POMDPs for robotic tasks with mixed observability RSS 2009 Posterior vs Parameter Sparsity in Latent Variable Models NeurIPS 2009 Probabilistic Relational PCA NeurIPS 2009 Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data JMLR 2009 Region-based Segmentation and Object Detection NeurIPS 2009