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» Estimating random variables from random sparse observations
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BMCBI
2008
186views more  BMCBI 2008»
13 years 7 months ago
Variable selection for large p small n regression models with incomplete data: Mapping QTL with epistases
Background: Identifying quantitative trait loci (QTL) for both additive and epistatic effects raises the statistical issue of selecting variables from a large number of candidates...
Min Zhang, Dabao Zhang, Martin T. Wells
CVPR
2007
IEEE
14 years 9 months ago
Kernel-based Tracking from a Probabilistic Viewpoint
In this paper, we present a probabilistic formulation of kernel-based tracking methods based upon maximum likelihood estimation. To this end, we view the coordinates for the pixel...
Quang Anh Nguyen, Antonio Robles-Kelly, Chunhua Sh...
CORR
2010
Springer
134views Education» more  CORR 2010»
13 years 6 months ago
The LASSO risk for gaussian matrices
We consider the problem of learning a coefficient vector x0 ∈ RN from noisy linear observation y = Ax0 + w ∈ Rn . In many contexts (ranging from model selection to image proce...
Mohsen Bayati, Andrea Montanari
FGR
2004
IEEE
105views Biometrics» more  FGR 2004»
13 years 11 months ago
Particle Filtering with Factorized Likelihoods for Tracking Facial Features
In the recent years particle filtering has been the dominant paradigm for tracking facial and body features, recognizing temporal events and reasoning in uncertainty. A major prob...
Ioannis Patras, Maja Pantic
MSOM
2010
65views more  MSOM 2010»
13 years 2 months ago
The Optimal Composition of Influenza Vaccines Subject to Random Production Yields
The Vaccine and Related Biologic Products Advisory Committee meets at least once a year to decide the composition of the influenza vaccine in the U.S. Past evidence suggests that ...
Soo-Haeng Cho