Variance Bounding of Delayed-Acceptance Kernels

A delayed-acceptance version of a Metropolis-Hastings algorithm can be useful for B ayesian inference when it is computationally expensive to calculate the true posterior, but a computationally cheap approximation is available; the delayed-acceptance kernel targets the same posterior as its associated parent Metropolis-Hastings kernel. Although the asymptotic variance of the ergodic average of any functional of the delayed-acceptance chain cannot be less than that obtained using its parent, the average computational time per iteration can be much smaller and so for a given computational budget the delayed-acceptance kernel can be more efficient. When the asymptotic variance of the ergodic averages of all L-2 functionals of the chain are finite, the kernel is said to be variance bounding. It has recently been noted that a delayed-acceptance kernel need not be variance bounding even when its parent is. We provide sufficient conditions for inheritance: for non-local algorithms, such as the independence sampler, the discrepancy between the log density of the approximation and that of the truth should be bounded; for local algorithms, two alternative sets of conditions are provided. As a by-product of our initial, general result we also supply sufficient conditions on any pair of proposals such that, for any shared target distribution, if a Metropolis-Hastings kernel using one of the proposals is variance bounding then so is the Metropolis-Hastings kernel using the other proposal.

Automated summary

A delayed-acceptance version of a Metropolis-Hastings algorithm is useful for Bayesian inference in cases where calculating the true posterior is computationally expensive but a cheaper approximation is available. The delayed-acceptance kernel can be more efficient than its parent in terms of computational time per iteration. Sufficient conditions for inheritance between different kernels are provided to ensure variance bounding.