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 Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

54
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
54
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

684
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
684
Convergence of Fourier Series01:21

Convergence of Fourier Series

148
The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...
148
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
81
Upsampling01:22

Upsampling

236
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
236
Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

53
The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
53

You might also read

Related Articles

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

Sort by
Same author

Longitudinal spatial neutrophil profiling during ACT in murine melanoma reveals distinct lymph node infiltration patterns.

NPJ systems biology and applications·2026
Same author

Data-driven model reveals increased stability of CAG-expanded huntingtin RNA due to MID1 binding.

PLoS computational biology·2026
Same author

Homing pigeon navigation relies on superparamagnetic macrophages under overcast conditions.

Science (New York, N.Y.)·2026
Same author

Multimodal data integration to determine viral and innate immune kinetics in human airway epithelium.

PLoS computational biology·2026
Same author

A tissue-intrinsic mechanism sensitizes HIV-1 particles for TLR-triggered innate immune responses.

Nature communications·2026
Same author

Suggested experimental design and computational modeling to infer single-cell lipid dynamics from a single destructive measurement.

iScience·2026
Same journal

Thymidylate synthase inhibitory drugs induce p53-dependent pathways differently.

PloS one·2026
Same journal

Top-down and bottom-up attention for joint pattern classification and reconstruction.

PloS one·2026
Same journal

Short- and long-term scaling behavior of blood pressure and pulse arrival time during sleep in healthy controls and patients with obstructive sleep apnea.

PloS one·2026
Same journal

Double DQN-based secrecy energy efficiency and fairness performance in IRS-assisted NOMA systems with friendly jamming.

PloS one·2026
Same journal

10 recommendations for strengthening citizen science for improved societal and ecological outcomes: A co-produced analysis of challenges and opportunities in the 21st century.

PloS one·2026
Same journal

Paying in public: Peer effects, impression management, and willingness to pay on digital payment platforms.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jul 2, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

551

A wall-time minimizing parallelization strategy for approximate Bayesian computation.

Emad Alamoudi1, Felipe Reck1, Nils Bundgaard2

  • 1Life and Medical Sciences Institute, University of Bonn, Bonn, Germany.

Plos One
|February 22, 2024
PubMed
Summary
This summary is machine-generated.

Look-ahead scheduling optimizes Approximate Bayesian Computation (ABC) Sequential Monte Carlo algorithms by preemptively sampling, minimizing computation time. This strategy fully utilizes computing resources, improving efficiency for parameter estimation in mechanistic models.

More Related Videos

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.2K
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.2K

Related Experiment Videos

Last Updated: Jul 2, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

551
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.2K
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.2K

Area of Science:

  • Computational Statistics
  • Statistical Modeling
  • High-Performance Computing

Background:

  • Approximate Bayesian Computation (ABC) is a popular method for parameter estimation in complex mechanistic models.
  • ABC analyses are computationally intensive, necessitating parallelization on high-performance computing (HPC) infrastructure.
  • Current parallelization strategies for ABC often result in underutilized computing resources and suboptimal efficiency.

Purpose of the Study:

  • To introduce and evaluate a novel parallelization strategy, 'look-ahead scheduling', for ABC Sequential Monte Carlo (SMC) algorithms.
  • To minimize wall-clock time and maximize resource utilization in computationally expensive ABC analyses.
  • To provide theoretical justification and empirical validation for the proposed preemptive sampling approach.

Main Methods:

  • Development of a look-ahead scheduling strategy involving preemptive sampling of subsequent generations in ABC-SMC.
  • Theoretical assessment and proof of unbiasedness for the preemptive sampling strategy.
  • Implementation and empirical evaluation of the strategy across diverse problems and varying numbers of parallel cores.

Main Results:

  • The look-ahead scheduling strategy effectively minimizes idle times of computing units by utilizing all available resources.
  • Theoretical assessment confirms the unbiasedness of the preemptive sampling approach.
  • Empirical evaluations demonstrate significant speed-ups, typically 10-20% and up to 50%, compared to established parallelization methods.

Conclusions:

  • Look-ahead scheduling offers a substantial improvement in the cost and run-time efficiency of ABC methods on HPC infrastructure.
  • The strategy is compatible with practical enhancements such as adaptive distance functions and summary statistic selection.
  • This optimized parallelization approach enables more efficient parameter estimation for mechanistic models using ABC.