50 search results - page 1 / 10 » Large Scale Bayesian Inference and Experimental Design for S... |
SIAMIS
2011
12 years 11 months ago
2011
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
JMLR
2008
13 years 4 months ago
2008
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...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
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. ...
NIPS
2007
13 years 5 months ago
2007
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the ...
SIAMSC
2011
12 years 11 months ago
2011
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...