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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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Fast Compression of MCMC Output.

Nicolas Chopin1, Gabriel Ducrocq1

  • 1Institut Polytechnique de Paris, ENSAE Paris, CEDEX, 92247 Malakoff, France.

Entropy (Basel, Switzerland)
|August 27, 2021
PubMed
Summary

We introduce cube thinning, a new method to compress Markov chain Monte Carlo (MCMC) outputs using control variates. This technique offers efficient sample compression without increasing computational complexity with sample size.

Area of Science:

  • Computational Statistics
  • Statistical Sampling Methods

Background:

  • Markov chain Monte Carlo (MCMC) methods are widely used for complex simulations.
  • Compressing MCMC output is crucial for efficient analysis and reducing computational burden.
  • Existing methods like Stein thinning can have computational complexity issues.

Purpose of the Study:

  • To propose a novel and efficient method for compressing MCMC algorithm outputs.
  • To leverage control variates for improved sample compression.
  • To develop a method with complexity independent of the compressed sample size.

Main Methods:

  • Cube thinning, a resampling technique.
  • Utilizing control variates to derive weights for resampling.
  • Imposing equality constraints on control variate averages via the cube method from survey sampling.
Keywords:
Markov chain Monte Carlocontrol variatesthinning

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Main Results:

  • Cube thinning enables resampling of MCMC samples with constraints.
  • The computational complexity of cube thinning is independent of the compressed sample size.
  • This offers a significant advantage over methods with quadratic complexity.

Conclusions:

  • Cube thinning provides an efficient alternative for MCMC output compression.
  • The method's complexity advantage makes it suitable for large-scale MCMC applications.
  • This approach enhances the practicality and scalability of MCMC analyses.