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

Particle MCMC algorithms and architectures for accelerating inference in state-space models.

Grigorios Mingas1, Leonardo Bottolo2, Christos-Savvas Bouganis1

  • 1Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK.

International Journal of Approximate Reasoning : Official Publication of the North American Fuzzy Information Processing Society
|April 5, 2017
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

Germline variants in <i>UHRF1</i> are associated with multilocus imprinting disturbance in humans and mice.

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

Bayesian causal graphical model for joint Mendelian randomization analysis of multiple exposures and outcomes.

American journal of human genetics·2025
Same author

Genetic coupling of enhancer activity and connectivity in gene expression control.

Nature communications·2025
Same author

Cell state-dependent allelic effects and contextual Mendelian randomization analysis for human brain phenotypes.

Nature genetics·2025
Same author

The penetrance of rare variants in cardiomyopathy-associated genes: A cross-sectional approach to estimating penetrance for secondary findings.

American journal of human genetics·2023
Same author

Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes.

American journal of human genetics·2023
Same journal

Corrigendum to "Estimating bounds on causal effects in high-dimensional and possibly confounded systems" [Int. J. Approx. Reason. 88 (2017) 371-384].

International journal of approximate reasoning : official publication of the North American Fuzzy Information Processing Society·2025
Same journal

<i>n</i>-Dimensional (<i>S</i>,<i>N</i>)-implications.

International journal of approximate reasoning : official publication of the North American Fuzzy Information Processing Society·2020
Same journal

A Bayesian Network Interpretation of the Cox's Proportional Hazard Model.

International journal of approximate reasoning : official publication of the North American Fuzzy Information Processing Society·2019
Same journal

A Constraint Optimization Approach to Causal Discovery from Subsampled Time Series Data.

International journal of approximate reasoning : official publication of the North American Fuzzy Information Processing Society·2018
Same journal

Estimating bounds on causal effects in high-dimensional and possibly confounded systems.

International journal of approximate reasoning : official publication of the North American Fuzzy Information Processing Society·2017
Same journal

Modeling Women's Menstrual Cycles using PICI Gates in Bayesian Network.

International journal of approximate reasoning : official publication of the North American Fuzzy Information Processing Society·2016
See all related articles

A new algorithm, particle Markov Chain Monte Carlo (pMCMC), and FPGA hardware accelerate sampling for complex State-Space Models (SSMs), enabling large-scale genetic data analysis.

Area of Science:

  • Computational Statistics
  • Bioinformatics
  • Hardware Acceleration

Background:

  • Particle Markov Chain Monte Carlo (pMCMC) is crucial for Bayesian inference in State-Space Models (SSMs).
  • High computational costs and poor multi-modal posterior performance limit pMCMC's scalability with large datasets.
  • Existing methods struggle with complex SSMs in scientific applications.

Purpose of the Study:

  • To enhance pMCMC efficiency for multi-modal posteriors using multiple chains (ppMCMC).
  • To develop custom parallel hardware architectures on FPGAs for pMCMC and ppMCMC.
  • To enable previously intractable large-scale SSM-based data analyses.

Main Methods:

  • Proposed a novel parallel pMCMC algorithm (ppMCMC) utilizing multiple Markov chains.
Keywords:
Bayesian inferenceField programmable gate arrayHardware accelerationMarkov Chain Monte CarloParticle filter

Related Experiment Videos

  • Designed and implemented custom parallel hardware architectures on Field Programmable Gate Arrays (FPGAs).
  • Evaluated the algorithm and architectures using a large-scale genetics case study.

    • Main Results:

    • ppMCMC demonstrated 1.96x higher sampling efficiency than pMCMC on CPUs.
    • FPGA implementations achieved significant speedups: pMCMC (12.1x CPU, 10.1x GPU) and ppMCMC (34.9x CPU, 41.8x GPU).
    • FPGA architectures offered substantial energy efficiency gains (pMCMC: 53x, ppMCMC: 173x).

    Conclusions:

    • The ppMCMC algorithm and FPGA architectures effectively address computational challenges in SSM analysis.
    • This work significantly advances the feasibility of large-scale, complex data analysis in fields like genetics.
    • The developed methods pave the way for more efficient and accessible Bayesian inference in scientific research.