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Related Concept Videos

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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.
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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)...
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...

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Related Experiment Video

Updated: Jun 19, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Model averaging techniques for quantifying conceptual model uncertainty.

Abhishek Singh1, Srikanta Mishra, Greg Ruskauff

  • 1INTERA Inc, Austin, TX, USA. asingh@intera.com

Ground Water
|November 3, 2009
PubMed
Summary

This study compares four groundwater model averaging techniques for conceptual model uncertainty. It assesses Generalized Likelihood Uncertainty Estimation (GLUE) and criterion-based methods to improve groundwater model predictions.

Related Experiment Videos

Last Updated: Jun 19, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Hydrogeology
  • Environmental Modeling
  • Statistical Analysis

Background:

  • Conceptual model uncertainty is a key challenge in groundwater modeling.
  • Statistical model averaging is crucial for assessing alternative model conceptualizations.
  • Existing techniques like GLUE, MLBMA, and AICMA have different underlying assumptions.

Purpose of the Study:

  • To comparatively assess four model averaging techniques for quantifying conceptual model uncertainty in groundwater models.
  • To examine the pros and cons of each technique from a practitioner's viewpoint.
  • To provide recommendations for using these techniques in groundwater modeling practice.

Main Methods:

  • Comparative assessment of four model averaging techniques: Generalized Likelihood Uncertainty Estimation (GLUE), Maximum Likelihood Bayesian Model Averaging (MLBMA) with Kashyap Information Criterion (KIC), MLBMA with Bayesian Information Criterion (BIC), and Akaike Information Criterion-based model averaging (AICMA).
  • Evaluation using two groundwater modeling case studies.
  • Analysis of statistical assumptions and their impact on model weights and ranks.

Main Results:

  • Different model averaging techniques yield significantly different relative model weights and ranks due to variations in statistical assumptions.
  • The study highlights the practical implications of these differences for groundwater model predictions.
  • Pros and cons of each method are identified through case study applications.

Conclusions:

  • Understanding the impact of statistical assumptions is critical when selecting a model averaging technique.
  • The choice of technique can influence the assessment of conceptual model uncertainty and subsequent model predictions.
  • Practitioners should carefully consider the strengths and weaknesses of each method for their specific groundwater modeling applications.