Abstract

For a broad class of multi-level models, there exist two well-known competing parameterizations, the centered parametrization (CP) and the non-centered parametrization (NCP), for effective MCMC implementation. Much literature has been devoted to the questions of when to use which and how to compromise between them via partial CP/NCP. This paper introduces an alternative strategy for boosting MCMC efficiency via simply interweaving— but not alternating—the two parameterizations. This strategy has the surprising property that failure of both the CP and NCP chains to converge geometrically does not prevent the interweaving algorithm from doing so. It achieves this seemingly magical property by taking advantage of the discordance of the two parameterizations, namely, the sufficiency of CP and the ancillarity of NCP, to substantially reduce the Markovian dependence, especially when the original CP and NCP form a “beauty and beast ” pair (i.e., when one chain mixes far more rapidly than the other). The ancillarity-sufficiency reformulation of the CP-NCP dichotomy allows us to borrow insight from the well-known Basu’s theorem on the independence of (complete) sufficient and ancillary statistics, albeit a Bayesian version of Basu’s

Citations