Probabilistic Modeling

Fourier Theoretic Probabilistic Inference over Permutations JMLR 2009 Gaussian process regression with Student-t likelihood NeurIPS 2009 Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation NeurIPS 2009 Highly scalable appearance-only SLAM - FAB-MAP 2.0 RSS 2009 Individuation, Identification and Object Discovery NeurIPS 2009 Inter-domain Gaussian Processes for Sparse Inference using Inducing Features NeurIPS 2009 Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions NeurIPS 2009 Learning a Small Mixture of Trees NeurIPS 2009 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 Localizing Bugs in Program Executions with Graphical Models NeurIPS 2009 Local Rules for Global MAP: When Do They Work ? NeurIPS 2009 Modeling the spacing effect in sequential category learning NeurIPS 2009 POMDPs for robotic tasks with mixed observability RSS 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 Rethinking LDA: Why Priors Matter NeurIPS 2009 Riffled Independence for Ranked Data NeurIPS 2009 Sensitivity analysis in HMMs with application to likelihood maximization NeurIPS 2009 Sequential effects reflect parallel learning of multiple environmental regularities NeurIPS 2009 Sparse and Locally Constant Gaussian Graphical Models NeurIPS 2009 Spatial Normalized Gamma Processes NeurIPS 2009 Structural inference affects depth perception in the context of potential occlusion NeurIPS 2009