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Related Concept Videos

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

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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

Bayesian methodology for model uncertainty using model performance data.

Enrique López Droguett1, Ali Mosleh

  • 1Department of Production Engineering, Federal University of Pernambuco, Brazil. ealopez@ufpe.br

Risk Analysis : an Official Publication of the Society for Risk Analysis
|September 17, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian method to assess model uncertainty, a major factor in prediction errors. It quantizes uncertainty from model structure and parameters for better risk analysis.

Related Experiment Videos

Last Updated: Jul 1, 2026

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

Area of Science:

  • Decision Science
  • Risk Analysis
  • Bayesian Statistics

Background:

  • Predictive models are characterized by structure and parameters.
  • Uncertainties in predictions stem from parameter uncertainty and model uncertainty.
  • Model uncertainty is often the primary contributor to overall prediction uncertainty.

Purpose of the Study:

  • To describe a Bayesian methodology for assessing model uncertainties.
  • To treat models as sources of information about an unknown of interest.
  • To specialize the framework for models providing point estimates and performance data.

Main Methods:

  • Developed a Bayesian framework for quantifying model uncertainty.
  • Specialized the framework for models with point estimates and performance data (observations vs. predictions).
  • Applied the methodology to physical models in fire risk analysis.

Main Results:

  • Demonstrated a systematic approach to assessing model uncertainty.
  • Showcased the application of Bayesian methods to quantify uncertainty from model structure.
  • Provided example applications in physical models for fire risk assessment.

Conclusions:

  • Bayesian methodology offers a robust framework for model uncertainty assessment.
  • Addressing model uncertainty is crucial for accurate risk analysis.
  • The proposed method is applicable to various physical models using performance data.