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

Approximate Integration01:24

Approximate Integration

79
In many practical and theoretical contexts, the exact value of a definite integral may be inaccessible. This limitation typically arises when the antiderivative of a function is either unknown or cannot be expressed in a closed mathematical form. Alternatively, it can occur when a function is defined not by a formula but by a finite set of empirical data points, such as those collected during experiments. In these cases, approximate integration techniques provide a valuable solution.One of the...
79
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

1.3K
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
1.3K
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

127
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
127
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.1K
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...
2.1K
State Function, Exact and Inexact Differentials01:27

State Function, Exact and Inexact Differentials

28
A state function is a thermodynamic property that depends solely on the current state of a system, irrespective of its history or how it arrived at that state. These functions are represented by capital letters, such as U, H, and S, which stand for internal energy, enthalpy, and entropy, respectively.For instance, the value of internal energy depends on the system's state variables and remains unaffected by the process path. This means that whether the system underwent a linear process or a...
28
Area Problem01:26

Area Problem

150
Determining the area of a region with straight edges is straightforward, as geometric formulas for rectangles, triangles, and polygons can be applied directly. However, traditional geometric methods are insufficient when a region has a curved boundary, such as the area under a function.fromThe area problem involves finding a systematic way to measure such regions. One approach to solving this problem is through approximation. Instead of attempting to compute the area exactly at the outset, the...
150

You might also read

Related Articles

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

Sort by
Same author

Joint likelihood-free inference of the number of selected single nucleotide polymorphisms and their selection coefficients in an evolving population.

Journal of theoretical biology·2026
Same author

A more complete picture: capturing single nucleotide variant diversity in extended-spectrum beta-lactamase producing <i>Escherichia coli</i> using post-enrichment metagenomics.

Microbial genomics·2026
Same author

Pre-clinical efficacy of a candidate outer membrane vesicle gonococcal vaccine in comparison with 4CMenB.

NPJ vaccines·2026
Same author

CRISPR-based environmental detection of Burkholderia pseudomallei identifies sanitation gaps and melioidosis risk in northeast Thailand.

Nature communications·2026
Same author

Bayesian network tool for analyzing the cost-effectiveness of bulk forensic trace DNA profiling.

Journal of forensic sciences·2026
Same author

Strain-level transmission inference across multi-kingdom metagenomic data using TRACS.

Nature microbiology·2026
Same journal

Diversification dynamics in the global radiation of gobies.

Systematic biology·2026
Same journal

Correction to: nQMaker: Estimating Time Nonreversible Amino Acid Substitution Models.

Systematic biology·2026
Same journal

Phylogenomic challenges in polyploid-rich lineages: Insights from paralog processing and reticulation methods using the complex genus Packera (Asteraceae: Senecioneae).

Systematic biology·2026
Same journal

An evolving view of phylogenetic biogeography.

Systematic biology·2026
Same journal

Modeling Site-and-Branch-Heterogeneity with GFmix.

Systematic biology·2026
Same journal

Coalescent-based branch length estimation improves dating of species trees.

Systematic biology·2026
See all related articles

Related Experiment Video

Updated: Mar 7, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

Fundamentals and Recent Developments in Approximate Bayesian Computation.

Jarno Lintusaari1,2, Michael U Gutmann1,2,3, Ritabrata Dutta1,2

  • 1Department of Computer Science, Aalto University, Espoo, Finland.

Systematic Biology
|February 9, 2017
PubMed
Summary
This summary is machine-generated.

Approximate Bayesian computation (ABC) offers a powerful framework for Bayesian inference when exact calculations are difficult. This method requires only the ability to simulate from a model, making it versatile for complex scientific problems.

More Related Videos

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Related Experiment Videos

Last Updated: Mar 7, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Area of Science:

  • * Bayesian inference is crucial in fields like phylogenetics and evolutionary biology.
  • * It provides a robust method for quantifying uncertainty in scientific models.

Background:

  • * Exact Bayesian inference is often intractable for complex models.
  • * Approximate Bayesian computation (ABC) offers a solution by minimizing assumptions.

Purpose of the Study:

  • * To explain the fundamentals of Approximate Bayesian computation (ABC).
  • * To review classical ABC algorithms and discuss recent advancements.
  • * To highlight ABC as a likelihood-free inference method.

Main Methods:

  • * ABC algorithms require only the ability to sample from a model.
  • * Focus on simulator-based and stochastic simulation models.
  • * Application in tree-based models and phylogenetics.

Main Results:

  • * ABC provides a principled framework for approximate quantitative answers.
  • * Enables inference for complex models where likelihoods are unknown.
  • * Facilitates the analysis of uncertainty with new evidence.

Conclusions:

  • * ABC is a valuable tool for likelihood-free inference in various scientific domains.
  • * Its minimal assumptions make it broadly applicable.
  • * Recent developments continue to enhance its utility in complex modeling.