Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation
近似贝叶斯计算 MCMC 核的方差有界性与几何遍历性
Approximate Bayesian computation has emerged as a standard computational tool when dealing with intractable likelihood functions in Bayesian inference. We show that many common Markov chain Monte Carlo kernels used to facilitate inference in this setting can fail to be variance bounding and hence geometrically ergodic, which can have consequences for the reliability of estimates in practice. This phenomenon is typically independent of the choice of tolerance in the…