Bayesian Experimental Design of Magnetic Resonance Imaging Sequences (2009)
Seeger, M.W., Nickisch, H., Pohmann, R., Schölkopf, B.
We show how improved sequences for magnetic resonance imaging can be found through automated optimization of Bayesian design scores. Combining recent advances in approximate Bayesian inference and...
Approximations for Binary Gaussian Process Classification (2008)
We provide a comprehensive overview of many recent algorithms for approximate inference in Gaussian process models for probabilistic binary classification. The relationships between several...
Compressed Sensing and Bayesian Experimental Design (2008)
Seeger, M.W., Nickisch, H., Cohen, W. W., McCallum, A., Roweis, S.
We relate compressed sensing (CS) with Bayesian experimental design and provide a novel efficient approximate method for the latter, based on expectation propagation. In a large comparative study...