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

Some adaptive monte carlo methods for Bayesian inference.

L Tierney1, A Mira

  • 1School of Statistics, University of Minnesota, Minneapolis, MN 55455, USA.

Statistics in Medicine
|September 4, 1999
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

Synergistic inhibition of pneumococcal growth by <i>Dolosigranulum pigrum</i> and <i>Corynebacterium pseudodiphtheriticum:</i> insights into nasopharyngeal microbial interactions.

Microbiology spectrum·2025
Same author

Nitrate: "the source makes the poison".

Critical reviews in food science and nutrition·2024
Same author

The Murine Oral Metatranscriptome Reveals Microbial and Host Signatures of Periodontal Disease.

Journal of dental research·2023
Same author

Cryoprotection of humanin-like peptides in seminal plasma for ejaculated spermatozoa of crossbred bulls.

Cryo letters·2023
Same author

The tongue biofilm metatranscriptome identifies metabolic pathways associated with the presence or absence of halitosis.

NPJ biofilms and microbiomes·2022
Same author

Unravelling groundwater and surface water sources in the Esteros del Iberá Wetland Area: An isotopic approach.

The Science of the total environment·2022
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
See all related articles

Markov chain Monte Carlo (MCMC) methods are vital for Bayesian inference. Developing adaptive strategies to automatically select and tune MCMC algorithms is crucial for improving efficiency and applicability in complex problems.

Area of Science:

  • Computational Statistics
  • Bayesian Inference
  • Algorithm Development

Background:

  • Markov chain Monte Carlo (MCMC) methods are increasingly essential for practical Bayesian inference.
  • A diverse array of MCMC algorithms exists, posing challenges in selecting the most suitable one for a given problem.
  • The performance of MCMC methods is highly dependent on algorithm choice and parameter tuning.

Purpose of the Study:

  • To explore the development of adaptive strategies for MCMC methods.
  • To address the challenge of selecting and adjusting MCMC algorithms for specific inference tasks.
  • To investigate methods that can dynamically optimize MCMC sampling based on problem characteristics and observed data.

Main Methods:

  • Outlining key issues and considerations in designing adaptive MCMC strategies.

Related Experiment Videos

  • Presenting preliminary results from exploratory adaptive MCMC approaches.
  • Discussing the integration of problem-specific information and sampling-derived information.

    • Main Results:

    • Preliminary findings suggest the feasibility of adaptive MCMC approaches.
    • Identified challenges in developing robust adaptive algorithms.
    • Demonstrated potential for improved sampling efficiency through adaptation.

    Conclusions:

    • Adaptive strategies hold promise for enhancing the practical application of MCMC methods.
    • Further research is needed to refine and validate adaptive MCMC techniques.
    • Developing automated, context-aware MCMC algorithms is a key future direction.