Automatic Variational ABC

Abstract

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.

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• Auto-Encoding Variational Bayes
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
• Adaptive approximate Bayesian computation
• Approximate Bayesian computation (ABC) gives exact results under the assumption of model error
• GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation
• Automatic Variational Inference in Stan

Cited by in corpus (5)

• Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
• Variational Bayes with Synthetic Likelihood
• Black-box Variational Inference for Stochastic Differential Equations
• Neural Density Estimation and Likelihood-free Inference
• Easy High-Dimensional Likelihood-Free Inference