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SIAMSC
2011
219views more  SIAMSC 2011»
13 years 18 hour ago
Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian
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...
ICDM
2010
IEEE
264views Data Mining» more  ICDM 2010»
13 years 3 months ago
Block-GP: Scalable Gaussian Process Regression for Multimodal Data
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...
Kamalika Das, Ashok N. Srivastava
ICASSP
2009
IEEE
13 years 11 months ago
A fast asymptotically efficient algorithm for blind separation of a linear mixture of block-wise stationary autoregressive proce
We propose a novel blind source separation algorithm called Block AutoRegressive Blind Identification (BARBI). The algorithm is asymptotically efficient in separation of instant...
Petr Tichavský, Arie Yeredor, Zbynek Koldov...
ICASSP
2011
IEEE
12 years 8 months ago
Motion vector recovery with Gaussian Process Regression
In this paper, we propose a Gaussian Process Regression (GPR) framework for concealment of corrupted motion vectors in predictive video coding of packet video systems. The problem...
Hadi Asheri, Abdolkhalegh Bayati, Hamid R. Rabiee,...
DSP
2008
13 years 5 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