Bayesian & Probabilistic

Approximate inference using planar graph decomposition NeurIPS 2006 A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG data NeurIPS 2006 A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests JMLR 2006 A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo NeurIPS 2006 Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure NeurIPS 2006 Bayesian Ensemble Learning NeurIPS 2006 Bayesian Image Super-resolution, Continued NeurIPS 2006 Bayesian Model Scoring in Markov Random Fields NeurIPS 2006 Bayesian Network Learning with Parameter Constraints JMLR 2006 Bayesian Policy Gradient Algorithms NeurIPS 2006 Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation JMLR 2006 Clustering Under Prior Knowledge with Application to Image Segmentation NeurIPS 2006 Comparative Gene Prediction using Conditional Random Fields 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 Differential Entropic Clustering of Multivariate Gaussians NeurIPS 2006 Dirichlet-Enhanced Spam Filtering based on Biased Samples NeurIPS 2006 Distributed Inference in Dynamical Systems NeurIPS 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 Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems 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