Laplace Matching for fast Approximate Inference in Latent Gaussian Models. (arXiv:2105.03109v2 [cs.LG] UPDATED)

Bayesian inference on non-Gaussian data is often non-analytic and requires
computationally expensive approximations such as sampling or variational
inference. We propose an approximate inference framework primarily designed to
be computationally cheap while still achieving high approximation quality. The
concept, which we call Laplace Matching, involves closed-form, approximate,
bi-directional transformations between the parameter spaces of exponential
families. These are constructed from Laplace approximations under
custom-designed basis transformations. The mappings can then be leveraged to
effectively turn a latent Gaussian distribution into …

approximate inference arxiv inference