On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
放宽容差并作事后校正的近似贝叶斯计算马尔可夫链蒙特卡洛
Summary Approximate Bayesian computation enables inference for complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo implementation of approximate Bayesian computation is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We propose an approach that involves using a relatively large tolerance for the Markov chain Monte Carlo sampler to ensure…