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Probabilistic Modeling

2591 directly classified papers

Papers per year

Papers

Gaussian Processes for Machine Learning (GPML) Toolbox JMLR 2010
Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation JMLR 2010
A Bayesian Analysis of Dynamics in Free Recall NIPS 2009
Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference NIPS 2009
Individuation, Identification and Object Discovery NIPS 2009
Fourier Theoretic Probabilistic Inference over Permutations JMLR 2009
Spatial Normalized Gamma Processes NIPS 2009
Highly scalable appearance-only SLAM - FAB-MAP 2.0 RSS 2009
Learning GP-BayesFilters via Gaussian process latent variable models RSS 2009
POMDPs for robotic tasks with mixed observability RSS 2009
Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data JMLR 2009
An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity JMLR 2009
The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models JMLR 2009
Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise NIPS 2009
Localizing Bugs in Program Executions with Graphical Models NIPS 2009
Structural inference affects depth perception in the context of potential occlusion NIPS 2009
Bayesian Source Localization with the Multivariate Laplace Prior NIPS 2009
Approximating MAP by Compensating for Structural Relaxations NIPS 2009
A joint maximum-entropy model for binary neural population patterns and continuous signals NIPS 2009
Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation NIPS 2009
Probabilistic Relational PCA NIPS 2009
Construction of Nonparametric Bayesian Models from Parametric Bayes Equations NIPS 2009
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs NIPS 2009
An LP View of the M-best MAP problem NIPS 2009
Riffled Independence for Ranked Data NIPS 2009