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PIMRC
2008
IEEE
16 years 17 days ago
Bayesian inference in linear models with a random Gaussian matrix : Algorithms and complexity
—We consider the Bayesian inference of a random Gaussian vector in a linear model with a random Gaussian matrix. We review two approaches to finding the MAP estimator for this m...
Ido Nevat, Gareth W. Peters, Jinhong Yuan
SIAMIS
2011
15 years 1 months ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
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
ICML
2009
IEEE
16 years 7 months ago
Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities
The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian proces...
Ryan Prescott Adams, Iain Murray, David J. C. MacK...
VISAPP
2010
15 years 4 months ago
Inverse Problems in Imaging and Computer Vision - From Regularization Theory to Bayesian Inference
phies are also mentioned and a common mathematical abstraction for all these inverses problems will be presented. By focusing on a simple linear forward model, first a synthetic an...
Ali Mohammad-Djafari
ICPR
2010
IEEE
16 years 1 months ago
Bayesian Inference for Nonnegative Matrix Factor Deconvolution Models
In this paper we develop a probabilistic interpretation and a full Bayesian inference for non-negative matrix deconvolution (NMFD) model. Our ultimate goal is unsupervised extract...
Serap Kirbiz, Ali Taylan Cemgil, Bilge Gunsel