Convergence of regression-adjusted approximate Bayesian computation

Wentao Li, Paul Fearnhead

Research output: Contribution to journalArticlepeer-review


We present asymptotic results for the regression-adjusted version of approximate Bayesian computation introduced by . We show that for an appropriate choice of the bandwidth, regression adjustment will lead to a posterior that, asymptotically, correctly quantifies uncertainty. Furthermore, for such a choice of bandwidth we can implement an importance sampling algorithm to sample from the posterior whose acceptance probability tends to unity as the data sample size increases. This compares favourably to results for standard approximate Bayesian computation, where the only way to obtain a posterior that correctly quantifies uncertainty is to choose a much smaller bandwidth, one for which the acceptance probability tends to zero and hence for which Monte Carlo error will dominate.
Original languageEnglish
Pages (from-to)301-318
Number of pages18
Issue number2
Publication statusPublished - 2018


Dive into the research topics of 'Convergence of regression-adjusted approximate Bayesian computation'. Together they form a unique fingerprint.

Cite this