Bayesian & Probabilistic

Bayesian Model Scoring in Markov Random Fields NeurIPS 2006 Bayesian Network Learning with Parameter Constraints JMLR 2006 Bayesian Policy Gradient Algorithms NeurIPS 2006 Causal inference in sensorimotor integration NeurIPS 2006 Combining causal and similarity-based reasoning NeurIPS 2006 Conditional mean field NeurIPS 2006 Context Effects in Category Learning: An Investigation of Four Probabilistic Models NeurIPS 2006 Data Integration for Classification Problems Employing Gaussian Process Priors NeurIPS 2006 Detecting Humans via Their Pose NeurIPS 2006 Dirichlet-Enhanced Spam Filtering based on Biased Samples NeurIPS 2006 Distributed Inference in Dynamical Systems NeurIPS 2006 Dynamic Imitation in a Humanoid Robot through Nonparametric Probabilistic Inference RSS 2006 Efficient Structure Learning of Markov Networks using L_1-Regularization NeurIPS 2006 Estimating the “Wrong” Graphical Model: Benefits in the Computation-Limited Setting JMLR 2006 Generalized Bradley-Terry Models and Multi-Class Probability Estimates JMLR 2006 Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space NeurIPS 2006 Hierarchical Dirichlet Processes with Random Effects NeurIPS 2006 Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions NeurIPS 2006 Learning Factor Graphs in Polynomial Time and Sample Complexity JMLR 2006 Learning Nonparametric Models for Probabilistic Imitation NeurIPS 2006 Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models NeurIPS 2006 Learning to be Bayesian without Supervision NeurIPS 2006 Linear Programming Relaxations and Belief Propagation -- An Empirical Study JMLR 2006 Linear Programs for Hypotheses Selection in Probabilistic Inference Models JMLR 2006 Mixture Regression for Covariate Shift NeurIPS 2006