Annals of Statistics
- Ann. Statist.
- Volume 37, Number 2 (2009), 697-725.
The pseudo-marginal approach for efficient Monte Carlo computations
We introduce a powerful and flexible MCMC algorithm for stochastic simulation. The method builds on a pseudo-marginal method originally introduced in [Genetics 164 (2003) 1139–1160], showing how algorithms which are approximations to an idealized marginal algorithm, can share the same marginal stationary distribution as the idealized method. Theoretical results are given describing the convergence properties of the proposed method, and simple numerical examples are given to illustrate the promising empirical characteristics of the technique. Interesting comparisons with a more obvious, but inexact, Monte Carlo approximation to the marginal algorithm, are also given.
Ann. Statist., Volume 37, Number 2 (2009), 697-725.
First available in Project Euclid: 10 March 2009
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 60J22: Computational methods in Markov chains [See also 65C40] 60K35: Interacting random processes; statistical mechanics type models; percolation theory [See also 82B43, 82C43]
Secondary: 60K35: Interacting random processes; statistical mechanics type models; percolation theory [See also 82B43, 82C43]
Andrieu, Christophe; Roberts, Gareth O. The pseudo-marginal approach for efficient Monte Carlo computations. Ann. Statist. 37 (2009), no. 2, 697--725. doi:10.1214/07-AOS574. https://projecteuclid.org/euclid.aos/1236693147