Abstract 

Approximate Bayesian computation (ABC) is an important methodology for Bayesian inference when the likelihood function is intractable. Sampling-based ABC algorithms such as rejectionand K2-ABC are inefficient when the parameters have high dimensions, while the regression-based algorithms such as Kand DR-ABC are hard to scale. In this paper, we introduce an optimization-based ABC framework that addresses these deficiencies. Leveraging a generative model for posterior and joint distribution matching, we show that ABC can be framed as saddle point problems, whose objectives can be accessed directly with samples. We present the predictive ABC algorithm (P-ABC), and provide a probabilistically approximately correct (PAC) bound for its learning consistency. Numerical experiment shows that P-ABC outperforms both K2and DR-ABC significantly.