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

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.1K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.1K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.9K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.9K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

4.5K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
4.5K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

7.2K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
7.2K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

6.1K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
6.1K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

558
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
558

You might also read

Related Articles

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

Sort by
Same author

Effects of the third dose of the coronavirus disease vaccine in healthy adults and immunosuppressed patients.

Scientific reports·2026
Same author

Attributional bias in epilepsy: Differences between genetic generalised epilepsy, temporal lobe epilepsy and healthy controls.

Epilepsy research·2026
Same author

Gender Disparities in Healthcare Utilization and Expenditures Among Single-Parent Households in Korea.

Healthcare (Basel, Switzerland)·2026
Same author

Unified theory resolves phenological paradoxes in biocollection data by modeling phenophase duration during Bayesian inference.

The New phytologist·2026
Same author

FDA Draft Guidance for the Use of Bayesian Methods in Clinical Trials.

JAMA·2026
Same author

Pathfinder: Parallel quasi-Newton variational inference.

Journal of machine learning research : JMLR·2026
Same journal

A Tree Perspective on Stick-Breaking Models in Covariate-Dependent Mixtures (with Discussion).

Bayesian analysis·2026
Same journal

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses.

Bayesian analysis·2026
Same journal

Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking.

Bayesian analysis·2026
Same journal

A Two-Component <i>G</i>-Prior for Variable Selection.

Bayesian analysis·2026
Same journal

Logistic-Beta Processes for Dependent Random Probabilities with Beta Marginals.

Bayesian analysis·2026
Same journal

Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data.

Bayesian analysis·2025
See all related articles
  1. Home
  3. Simulation-based Calibration Checking For Bayesian Computation: The Choice Of Test Quantities Shapes Sensitivity.
  1. Home
  3. Simulation-based Calibration Checking For Bayesian Computation: The Choice Of Test Quantities Shapes Sensitivity.

Related Experiment Video

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.2K

Simulation-Based Calibration Checking for Bayesian Computation: The Choice of Test Quantities Shapes Sensitivity.

Martin Modrák1, Angie H Moon2, Shinyoung Kim3

  • 1Institute of Microbiology of the Czech Academy of Sciences.

Bayesian Analysis
|October 6, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

We introduce a new simulation-based calibration checking (SBC) method to validate posterior distributions. This enhanced approach detects more issues than previous methods, including when the posterior equals the prior, by using new data-dependent test quantities.

Keywords:
62C10calibrationprobabilistic programmingsoftware testing

More Related Videos

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

2.9K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.4K

Related Experiment 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

2.9K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.4K

Area of Science:

  • Computational Statistics
  • Bayesian Inference

Background:

  • Simulation-based calibration checking (SBC) is crucial for validating posterior distributions from computational models.
  • Existing SBC methods have limitations in detecting certain classes of posterior distribution errors, such as when the posterior is indistinguishable from the prior.

Purpose of the Study:

  • To introduce a novel variant of simulation-based calibration checking (SBC) that enhances the detection of errors in posterior distributions.
  • To address limitations of previous SBC implementations, enabling the identification of a broader range of potential issues.

Main Methods:

  • Development of a new SBC variant incorporating additional data-dependent test quantities.
  • Theoretical analysis of the enhanced SBC method to understand its statistical underpinnings.
  • Investigation of the joint likelihood of the data as a key test quantity.
  • Numerical case studies using a multivariate normal distribution and an ordered simplex data type with Hamiltonian Monte Carlo.

    • Main Results:

    • The proposed SBC variant can detect a wider range of posterior distribution problems compared to existing methods.
    • The joint likelihood of the data is demonstrated as a powerful test quantity for SBC.
    • Theoretical analysis provides a deeper understanding of SBC mechanisms.
    • Case studies validate the practical utility and effectiveness of the new SBC approach.

    Conclusions:

    • The enhanced SBC variant offers a more comprehensive validation of posterior distributions in computational statistics.
    • The inclusion of specific data-dependent test quantities significantly improves the diagnostic power of SBC.
    • This work clarifies common misconceptions and provides a robust tool for Bayesian inference validation.