Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models. (arXiv:2108.12657v2 [cs.LG] UPDATED)

Fast inference of numerical model parameters from data is an important
prerequisite to generate predictive models for a wide range of applications.
Use of sampling-based approaches such as Markov chain Monte Carlo may become
intractable when each likelihood evaluation is computationally expensive. New
approaches combining variational inference with normalizing flow are
characterized by a computational cost that grows only linearly with the
dimensionality of the latent variable space, and rely on gradient-based
optimization instead of sampling, providing a more efficient …

arxiv flow inference