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← Bayesian & Probabilistic
Machine Learning › Bayesian & Probabilistic ›

Probabilistic Modeling

2,252 papers

Papers per year

5
84
58
212
96
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Papers

Approximate inference using planar graph decomposition NIPS 2006
Context Effects in Category Learning: An Investigation of Four Probabilistic Models NIPS 2006
An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments NIPS 2006
The Neurodynamics of Belief Propagation on Binary Markov Random Fields NIPS 2006
Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing NIPS 2006
Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype NIPS 2006
Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields NIPS 2006
Learning to parse images of articulated bodies NIPS 2006
Efficient Structure Learning of Markov Networks using $L_1$-Regularization NIPS 2006
Mixture Regression for Covariate Shift NIPS 2006
Clustering Under Prior Knowledge with Application to Image Segmentation NIPS 2006
Inferring Network Structure from Co-Occurrences NIPS 2006
Uncertainty, phase and oscillatory hippocampal recall NIPS 2006
Learning Structural Equation Models for fMRI NIPS 2006
Simplifying Mixture Models through Function Approximation NIPS 2006
Multi-dynamic Bayesian Networks NIPS 2006
Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model NIPS 2006
Conditional mean field NIPS 2006
Distributed Inference in Dynamical Systems NIPS 2006
Generalized Bradley-Terry Models and Multi-Class Probability Estimates JMLR 2006
Segmental Hidden Markov Models with Random Effects for Waveform Modeling JMLR 2006
Linear Programs for Hypotheses Selection in Probabilistic Inference Models JMLR 2006
Bayesian Network Learning with Parameter Constraints JMLR 2006
Learning Factor Graphs in Polynomial Time and Sample Complexity JMLR 2006
A Linear Non-Gaussian Acyclic Model for Causal Discovery JMLR 2006