Sparse linear models: Variational approximate inference and Bayesian experimental design
Identifieur interne : 000537 ( Main/Merge ); précédent : 000536; suivant : 000538Sparse linear models: Variational approximate inference and Bayesian experimental design
Auteurs :Source :
- Journal of Physics: Conference Series [ 1742-6596 ] ; 2009-12-01.
English descriptors
- Teeft :
- Algorithm, Approximate bayesian inference, Approximation, Bayesian, Bayesian inference, Biological cybernetics, Bulk variances, Conference series, Convex, Convex optimization, Covariance, David wipf, Design optimization, Double loop algorithm, Estimation, Exact sparsity, Expectation propagation, Experimental design, Future research, Gaussian, Gaussian family, Gaussian functions, General idea, Hard problem, Ieee trans, Image data, Image models, Image reconstruction, Image statistics, Inference, Informatics, Information gain, International workshop, Laplacian potentials, Large models, Linear models, Linear system, Model structure, Neural comput, Neural information processing systems, Numerical mathematics, Omni press, Optimization, Other words, Outer loop updates, Partition function, Phase encodes, Planck institute, Posterior, Posterior covariance, Posterior moments, Publishing journal, Relative entropy, Same model, Sampling design, Sampling design optimization, Sampling optimization, Scalable, Scalable algorithms, Scale mixture decompositions, Scale mixtures, Second term, Single site, Smallest eigenvalues, Sparse, Sparse bayesian inference, Sparse estimation, Sparse inference, Sparsity, Stationary point, Stationary points, Undersampled reconstruction, Variance, Variances computation, Variances computations, Variational, Variational approximations, Variational bayesian approximations, Variational parameters, Variational problem, Variational relaxations, Wide range.
Url:
DOI: 10.1088/1742-6596/197/1/012001
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