Seeger, MatthiasNickisch, HannesPohmann, RolfSchoelkopf, Bernhard2010-12-012010-12-012010-12-012009https://infoscience.epfl.ch/handle/20.500.14299/61735We show how improved sequences for magnetic resonance imaging can be found through optimization of Bayesian design scores. Combining approximate Bayesian inference and natural image statistics with high-performance numerical computation, we propose the first Bayesian experimental design framework for this problem of high relevance to clinical and brain research. Our solution requires large-scale approximate inference for dense, non-Gaussian models. We propose a novel scalable variational inference algorithm, and show how powerful methods of numerical mathematics can be modified to compute primitives in our framework. Our approach is evaluated on raw data from a 3T MR scanner.Bayesian learningMagnetic resonance imagingExperimental designAdaptive samplingVariational inferenceConvex optimizationtext::conference output::conference proceedings::conference paper