Search Results for author:
Found 3 papers, 2 papers with code
Bayesian Semi-structured Subspace Inference
To this end, we extend subspace inference for joint posterior sampling from a full parameter space for structured effects and a subspace for unstructured effects.
Bernstein Flows for Flexible Posteriors in Variational Bayes
Variational inference (VI) is a technique to approximate difficult to compute posteriors by optimization.
Transformation Models for Flexible Posteriors in Variational Bayes
In Bayesian neural networks, variational inference is widely used to approximate difficult-to-compute posteriors by variational distributions.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
