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Paper No. 15-04

Download 15-03

Medina-Aguayo FJ, Lee A and Roberts GO

Stability of Noisy Metropolis-Hastings

Abstract: Pseudo-marginal Markov chain Monte Carlo methods for sampling from intractable distributions have gained recent interest and have been theoretically studied in considerable depth. Their main appeal is that they are exact, in the sense that they target marginally the correct invariant distribution. However, the pseudo-marginal Markov chain can exhibit poor mixing and slow convergence towards its target. As an alternative, a subtly dierent Markov chain can be simulated, where better mixing is possible but the exactness property is sacriced. This is the noisy algorithm, initially conceptualised as Monte Carlo within Metropolis (MCWM), which has also been studied but to a lesser extent. The present article provides a further characterisation of the noisy algorithm, with a focus on fundamental stability properties like positive recurrence and geometric ergodicity. Sucient conditions for inheriting geometric ergodicity from a standard Metropolis{Hastings chain are given, as well as convergence of the invariant distribution towards the true target distribution.

Keywords: Markov chain Monte Carlo; Pseudo-marginal Monte Carlo; Monte Carlo within Metropolis; Intractable likelihoods; Geometric ergodicity.