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» Distributed sparse linear regression
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DSP
2008
13 years 10 months ago
Empirical Bayes linear regression with unknown model order
We study maximum a posteriori probability model order selection for linear regression models, assuming Gaussian distributed noise and coefficient vectors. For the same data model,...
Yngve Selén, Erik G. Larsson
BMCBI
2006
187views more  BMCBI 2006»
13 years 10 months ago
Detecting outliers when fitting data with nonlinear regression - a new method based on robust nonlinear regression and the false
Background: Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads ...
Harvey J. Motulsky, Ronald E. Brown
ESSMAC
2003
Springer
14 years 3 months ago
Analysis of Some Methods for Reduced Rank Gaussian Process Regression
Abstract. While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational com...
Joaquin Quiñonero Candela, Carl Edward Rasm...
TASLP
2010
126views more  TASLP 2010»
13 years 4 months ago
Sound Field Reproduction using the Lasso
Reproducing a sampled sound field using an array of loudspeakers is a problem with well-appreciated applications to acoustics and ultrasound treatment. Loudspeaker signal design ha...
G. N. Lilis, Daniele Angelosante, Georgios B. Gian...
KDD
2008
ACM
183views Data Mining» more  KDD 2008»
14 years 10 months ago
A bayesian mixture model with linear regression mixing proportions
Classic mixture models assume that the prevalence of the various mixture components is fixed and does not vary over time. This presents problems for applications where the goal is...
Xiuyao Song, Chris Jermaine, Sanjay Ranka, John Gu...