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    ICML
    2009
    IEEE
    188views Machine Learning» more  ICML 2009»
    Convex variational Bayesian inference for large scale generalized linear models
    14 years 5 months ago
    Convex variational Bayesian inference for large scale generalized linear models
    Download  www.kyb.tuebingen.mpg.de
    We show how variational Bayesian inference can be implemented for very large generalized linear models. Our relaxation is proven to be a convex problem for any log-concave model. ...
    Hannes Nickisch, Matthias W. Seeger
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    SIAMIS
    2011
    237views Software Engineering» more  SIAMIS 2011»
    Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
    12 years 11 months ago
    Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
    Download  lapmal.epfl.ch
    Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
    Matthias W. Seeger, Hannes Nickisch
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    DSP
    2007
    137views Emerging Technology» more  DSP 2007»
    Variational and stochastic inference for Bayesian source separation
    13 years 4 months ago
    Variational and stochastic inference for Bayesian source separation
    Download  perso.telecom-paristech.fr
    We tackle the general linear instantaneous model (possibly underdetermined and noisy) where we model the source prior with a Student t distribution. The conjugate-exponential char...
    Ali Taylan Cemgil, Cédric Févotte, S...
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    SIAMSC
    2011
    219views more  SIAMSC 2011»
    Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian
    12 years 11 months ago
    Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian
    Download  lucaswilcox.com
    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...
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    JMLR
    2008
    209views more  JMLR 2008»
    Bayesian Inference and Optimal Design for the Sparse Linear Model
    13 years 4 months ago
    Bayesian Inference and Optimal Design for the Sparse Linear Model
    Download  www.kyb.tuebingen.mpg.de
    The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
    Matthias W. Seeger
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