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» Graphical Models: Statistical inference vs. determination
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NIPS
2007
13 years 8 months ago
Measuring Neural Synchrony by Message Passing
A novel approach to measure the interdependence of two time series is proposed, referred to as “stochastic event synchrony” (SES); it quantifies the alignment of two point pr...
Justin Dauwels, François B. Vialatte, Tomas...
ISBI
2006
IEEE
14 years 8 months ago
A novel approximate inference approach to automated classification of protein subcellular location patterns in multi-cell images
The subcellular location of proteins is most often determined by visual interpretation of fluorescence microscope images. In recent years, automated systems have been developed so...
Shann-Ching Chen, Geoffrey J. Gordon, Robert F. Mu...
ICML
2004
IEEE
14 years 8 months ago
Variational methods for the Dirichlet process
Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While ...
David M. Blei, Michael I. Jordan
NIPS
2003
13 years 8 months ago
Ambiguous Model Learning Made Unambiguous with 1/f Priors
What happens to the optimal interpretation of noisy data when there exists more than one equally plausible interpretation of the data? In a Bayesian model-learning framework the a...
Gurinder S. Atwal, William Bialek
NECO
2008
134views more  NECO 2008»
13 years 7 months ago
Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach
One of the central problems in cognitive science is determining the mental representations that underlie human inferences. Solutions to this problem often rely on the analysis of ...
Daniel J. Navarro, Thomas L. Griffiths