Variational inference is a family of optimisation-based methods for approximating complex posterior distributions in Bayesian models. By transforming inference into an optimisation problem, these ...

Probabilistic inference in data-driven models is promising for predicting outputs and associated confidence levels, alleviating risks arising from overconfidence ...

Researchers have developed an importance-sampling-based black box variational inference algorithm that reuses earlier ...