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JAIR
1998
198views more  JAIR 1998»
13 years 7 months ago
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Alberto Ruiz, Pedro E. López-de-Teruel, M. ...
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...
ICML
2008
IEEE
14 years 8 months ago
Efficiently learning linear-linear exponential family predictive representations of state
Exponential Family PSR (EFPSR) models capture stochastic dynamical systems by representing state as the parameters of an exponential family distribution over a shortterm window of...
David Wingate, Satinder P. Singh
SIAMSC
2011
219views more  SIAMSC 2011»
13 years 2 months ago
Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian
We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
H. P. Flath, Lucas C. Wilcox, Volkan Akcelik, Judi...
CVBIA
2005
Springer
14 years 29 days ago
A Hybrid Framework for Image Segmentation Using Probabilistic Integration of Heterogeneous Constraints
In this paper we present a new framework for image segmentation using probabilistic multinets. We apply this framework to integration of regionbased and contour-based segmentation ...
Rui Huang, Vladimir Pavlovic, Dimitris N. Metaxas