Improving Approximate Bayesian Computation with Pre-judgment Rule

Approximate Bayesian Computation (ABC) is a popular approach for Bayesian modeling, when these models exhibit an intractable likelihood. However, during each proposal of ABC, a great number of simulators are required and each simulation is always time-consuming. The overall goal of this work is to avoid inefficient computational cost of ABC. A pre-judgment rule (PJR) is proposed, which mainly aims to judge the acceptance condition using a small fraction of simulators instead of the whole simulators, thus achieving less computational complexity. In addition, it provided a theoretical study of the error bounded caused by PJR Strategy. Finally, the methodology was illustrated with various examples. The empirical results show both the effectiveness and efficiency of PJR compared with the previous methods.