Annals of Statistics
- Ann. Statist.
- Volume 38, Number 6 (2010), 3387-3411.
Sequentially interacting Markov chain Monte Carlo methods
Sequential Monte Carlo (SMC) is a methodology for sampling approximately from a sequence of probability distributions of increasing dimension and estimating their normalizing constants. We propose here an alternative methodology named Sequentially Interacting Markov Chain Monte Carlo (SIMCMC). SIMCMC methods work by generating interacting non-Markovian sequences which behave asymptotically like independent Metropolis–Hastings (MH) Markov chains with the desired limiting distributions. Contrary to SMC, SIMCMC allows us to iteratively improve our estimates in an MCMC-like fashion. We establish convergence results under realistic verifiable assumptions and demonstrate its performance on several examples arising in Bayesian time series analysis.
Ann. Statist., Volume 38, Number 6 (2010), 3387-3411.
First available in Project Euclid: 30 November 2010
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 65C05: Monte Carlo methods 60J05: Discrete-time Markov processes on general state spaces
Secondary: 62F15: Bayesian inference
Brockwell, Anthony; Del Moral, Pierre; Doucet, Arnaud. Sequentially interacting Markov chain Monte Carlo methods. Ann. Statist. 38 (2010), no. 6, 3387--3411. doi:10.1214/09-AOS747. https://projecteuclid.org/euclid.aos/1291126961