A Generalized Tangent Approximation based Variational Inference Framework for Strongly Super-Gaussian Likelihoods

Variational inference, as an alternative to Markov chain Monte Carlo sampling, has played a transformative role in enabling scalable computation for complex Bayesian models. Nevertheless, existing approaches often depend on either rigid model-specific formulations or stochastic black-box optimizatio