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Metric Gaussian Variational Inference
[article]
2020
arXiv
pre-print
Solving Bayesian inference problems approximately with variational approaches can provide fast and accurate results. Capturing correlation within the approximation requires an explicit parametrization. This intrinsically limits this approach to either moderately dimensional problems, or requiring the strongly simplifying mean-field approach. We propose Metric Gaussian Variational Inference (MGVI) as a method that goes beyond mean-field. Here correlations between all model parameters are taken
arXiv:1901.11033v3
fatcat:4xth43f4mzaanir4rwr5hufq2i