Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Efficient Monte Carlo sampling by parallel marginalization.

Jonathan Weare1

  • 1Department of Mathematics, University of California, Berkeley, CA 94720, USA. weare@math.berkeley.edu

Proceedings of the National Academy of Sciences of the United States of America
|July 21, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An Exact Multiple-Time-Step Variational Formulation for the Committor and the Transition Rate.

The journal of physical chemistry. B·2025
Same author

Can AI weather models predict out-of-distribution gray swan tropical cyclones?

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics.

The Journal of chemical physics·2025
Same author

Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics.

ArXiv·2025
Same author

BAD-NEUS: Rapidly converging trajectory stratification.

The Journal of chemical physics·2024
Same author

Accurate estimates of dynamical statistics using memory.

The Journal of chemical physics·2024
Same journal

Tau protein as a regulator of mitochondrial function and dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A scalable, dividing cell model for the robust propagation and quantification of human sporadic Creutzfeldt-Jakob disease prions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Epigenetic regulation of mesenchymal BMP signaling directs postnatal organ innervation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Single-shot wide-field biochemical imaging at 1 kHz frame rate.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Morphogenesis and topological evolution of a frustrated nematic liquid crystal under confinement.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

B cell-intrinsic CXCR3 drives efficient generation of ectopic pulmonary germinal center responses to influenza A virus infection.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

This study introduces a novel Markov chain Monte Carlo (MCMC) sampling method that accelerates independent sample generation. By using coarse Markov chains, it significantly reduces correlation times in complex systems.

Area of Science:

  • Computational Statistics
  • Statistical Physics
  • Applied Mathematics

Background:

  • Markov chain Monte Carlo (MCMC) methods are crucial for statistical inference but often face challenges with long correlation times.
  • Extended correlation times necessitate numerous simulation steps, increasing computational cost and time for generating independent samples.

Purpose of the Study:

  • To propose an efficient MCMC sampling method that overcomes the limitation of long correlation times.
  • To accelerate the generation of independent samples in complex systems.

Main Methods:

  • The proposed method employs auxiliary coarse Markov chains that rapidly equilibrate and sample marginal distributions.
  • Information is transferred between the full system's Markov chain and these auxiliary chains via exchanges.

Related Experiment Videos

  • The approach is tested on bridge sampling and filtering/smoothing problems for stochastic differential equations.

    • Main Results:

    • Numerical tests demonstrate the effectiveness of the proposed method in reducing correlation times.
    • The method successfully addresses challenges in bridge sampling and filtering/smoothing for stochastic differential equations.
    • Faster generation of independent samples is achieved compared to traditional MCMC techniques.

    Conclusions:

    • The novel MCMC method effectively reduces correlation times, leading to more efficient sampling.
    • This approach offers a significant improvement for computational tasks involving stochastic differential equations.
    • The technique provides a valuable tool for accelerating statistical inference in complex models.