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Fast Compression of MCMC Output

by *,† and
Institut Polytechnique de Paris, ENSAE Paris, CEDEX, 92247 Malakoff, France
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Cathy W. S. Chen
Entropy 2021, 23(8), 1017;
Received: 6 July 2021 / Revised: 3 August 2021 / Accepted: 3 August 2021 / Published: 6 August 2021
(This article belongs to the Special Issue Approximate Bayesian Inference)
We propose cube thinning, a novel method for compressing the output of an MCMC (Markov chain Monte Carlo) algorithm when control variates are available. It allows resampling of the initial MCMC sample (according to weights derived from control variates), while imposing equality constraints on the averages of these control variates, using the cube method (an approach that originates from survey sampling). The main advantage of cube thinning is that its complexity does not depend on the size of the compressed sample. This compares favourably to previous methods, such as Stein thinning, the complexity of which is quadratic in that quantity. View Full-Text
Keywords: control variates; Markov chain Monte Carlo; thinning control variates; Markov chain Monte Carlo; thinning
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MDPI and ACS Style

Chopin, N.; Ducrocq, G. Fast Compression of MCMC Output. Entropy 2021, 23, 1017.

AMA Style

Chopin N, Ducrocq G. Fast Compression of MCMC Output. Entropy. 2021; 23(8):1017.

Chicago/Turabian Style

Chopin, Nicolas, and Gabriel Ducrocq. 2021. "Fast Compression of MCMC Output" Entropy 23, no. 8: 1017.

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