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Found 5 papers, 1 papers with code
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
We present a Bayesian approach to identify optimal transformations that map model input points to low dimensional latent variables.
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the computational efficiency and accuracy under a limited number of model simulations.
Compressive sensing adaptation for polynomial chaos expansions
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ.
Efficient Bayesian experimentation using an expected information gain lower bound
We propose a framework where a lower bound of the expected information gain is used as an alternative design criterion.
Variational Reformulation of Bayesian Inverse Problems
The classical approach to inverse problems is based on the optimization of a misfit function.
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