An improved approximate Bayesian computation scheme for parameter inference based on a recalibration post-processing method

Approximate Bayesian Computation algorithms (ABC), bypassing the intractable likelihood function, are commonly used to estimate the posterior distributions of parameters in practice. However ABC algorithms also have some inherent problems in the application, for example, when the algorithm maintains operating efficiency, it is difficult to obtain a more accurate approximation of the target distribution. To overcome these defects, an improved ABC algorithm is introduced in this article. The novel algorithm based on the ABC Sequential Monte Carlo (ABC-SMC) and a new recalibration post-processing methods is obtained to estimate the parameters of the biodynamic models, which is also called the ABC-SMCR algorithm. Through this article, it will be shown that the new algorithm is promising in processing the parameter inference problem. This algorithm not only offers high computational efficiency but also improves the quality estimate of the posterior distributions. To demonstrate its strengths, two examples are given. The first illustrative example on the basis of simulated data mainly demonstrates that it can be used to obtain accurate parameter inference of the Lotka-Volterra model. The second illustration based on actual flu outbreak data are given to show the computational efficiency of this algorithm in parameter estimation of the epidemic model.