journal article Jul 26, 2021

Rao–Blackwellisation in the Markov Chain Monte Carlo Era

View at Publisher Save 10.1111/insr.12463
Abstract
SummaryRao–Blackwellisation is a notion often occurring in the MCMC literature, with possibly different meanings and connections with the original Rao–Blackwell theorem as established by C.R. Rao in 1945 and D. Blackwell in 1947, including a reduction of the variance of the resulting Monte Carlo approximations. This survey reviews some of the meanings of the term.
Topics

No keywords indexed for this article. Browse by subject →

References
69
[1]
Andrieu C. deFreitas N.&Doucet A.(2001).Rao–Blackwellised particle filtering via data augmentation. InAdvances in Neural Information Processing Systems (NIPS) pp.561–567. Vancouver Canada.
[3]
Atchadé Y. "Improving on the independent Metropolis–Hastings algorithm" Stat. Sin. (2005)
[5]
Berg S. Zhu J.&Clayton M.K.(2019).Control variates and Rao–Blackwellization for deterministic sweep Markov chains. arXiv:1912.06926.
[11]
Branchini N. Optimized auxiliary particle filters. arXiv (2020)
[20]
Sequential Monte Carlo Samplers

Pierre Del Moral, Arnaud Doucet, Ajay Jasra

Journal of the Royal Statistical Society Series B:... 10.1111/j.1467-9868.2006.00553.x
[22]
Doucet A. (1999)
[23]
Doucet A. (2000)
[26]
Fearnhead P. ŁAtuszynski K. Roberts G.O.&Sermaidis G.(2017).Continious‐time importance sampling: Monte Carlo methods which avoid time‐discretisation error. arXiv:1712.06201.
[28]
Ga˙semyr J.(2002).Markov chain Monte Carlo algorithms with independent proposal distribution and their relation to importance sampling and rejection sampling Department of Statistics Univ. of Oslo.
[29]
Sampling-Based Approaches to Calculating Marginal Densities

Alan E. Gelfand, Adrian F. M. Smith

Journal of the American Statistical Association 10.1080/01621459.1990.10476213
[30]
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

Stuart Geman, Donald Geman

IEEE Transactions on Pattern Analysis and Machine... 10.1109/tpami.1984.4767596
[31]
Geyer C.(1993).Estimating normalizing constants and reweighting mixtures in Markov chain Monte Carlo School of Statistics Univ. of Minnesota.
[32]
Geyer C. "Conditioning in Markov chain Monte Carlo" J. Comput. Graph. Statis. (1994) 10.1080/10618600.1995.10474672
[33]
Novel approach to nonlinear/non-Gaussian Bayesian state estimation

N.J. Gordon, D.J. Salmond, A.F.M. Smith

IEE Proceedings F Radar and Signal Processing 10.1049/ip-f-2.1993.0015
[35]
Gutmann M.U. "Noise‐contrastive estimation of unnormalized statistical models, with applications to natural image statistics" J. Mach. Learn. Res. (2012)
[36]
Monte Carlo sampling methods using Markov chains and their applications

W. K. Hastings

Biometrika 10.1093/biomet/57.1.97
[40]
Klaas M. deFreitas N.&Doucet A.(2005).Toward practicaln2Monte Carlo: the marginal particle filter. InProceedings of the Twenty‐First Conference on Uncertainty in Artificial Intelligence (UAI2005).
[41]
A Theory of Statistical Models for Monte Carlo Integration

A. Kong, P. McCullagh, X.-L. Meng et al.

Journal of the Royal Statistical Society Series B:... 10.1111/1467-9868.00404
[42]
Lehmann E. (1998)
[44]
Lindsten F. (2011)
[45]
An explicit variance reduction expression for the Rao-Blackwellised particle filter

Fredrik Lindsten, Thomas B. Schön, Jimmy Olsson

IFAC Proceedings Volumes 10.3182/20110828-6-it-1002.02920
[50]
Marin J. (2011)

Showing 50 of 69 references

Metrics
5
Citations
69
References
Details
Published
Jul 26, 2021
Vol/Issue
89(2)
Pages
237-249
License
View
Cite This Article
Christian P. Robert, Gareth Roberts (2021). Rao–Blackwellisation in the Markov Chain Monte Carlo Era. International Statistical Review / Revue Internationale de Statistique, 89(2), 237-249. https://doi.org/10.1111/insr.12463