Automatic Variational ABC
This addresses efficiency issues for researchers using likelihood-free Bayesian inference with simulation models, though it appears incremental as it builds on existing variance reduction techniques.
The paper tackled the problem of high variance in gradient estimators for Stochastic Variational Inference (SVI) when used with Approximate Bayesian Computation (ABC), which limits efficiency. The result was a new algorithm that produces low-variance gradient estimators, demonstrated to be correct and efficient on three problems compared to existing SVI algorithms.
Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models. Stochastic Variational inference (SVI) is an appealing alternative to the inefficient sampling approaches commonly used in ABC. However, SVI is highly sensitive to the variance of the gradient estimators, and this problem is exacerbated by approximating the likelihood. We draw upon recent advances in variance reduction for SV and likelihood-free inference using deterministic simulations to produce low variance gradient estimators of the variational lower-bound. By then exploiting automatic differentiation libraries we can avoid nearly all model-specific derivations. We demonstrate performance on three problems and compare to existing SVI algorithms. Our results demonstrate the correctness and efficiency of our algorithm.