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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...
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.
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...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
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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Related Experiment Video

Updated: May 16, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Improving default risk prediction using Bayesian model uncertainty techniques.

Reza Kazemi1, Ali Mosleh

  • 1Center for Risk and Reliability, University of Maryland, College Park, Maryland, USA.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|November 21, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian framework to improve credit risk assessment by incorporating rating agency accuracy. The new model enhances probability of default predictions, outperforming individual agency estimates and accounting for major financial events.

Related Experiment Videos

Last Updated: May 16, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Quantitative Finance
  • Risk Management
  • Statistical Modeling

Background:

  • Credit risk, defined as potential exposure to obligor default, is crucial for financial institutions.
  • Existing credit risk models, including those from rating agencies, face limitations in accuracy and handling market volatility.
  • Regulatory capital requirements (e.g., Basel Committee on Banking Supervision) necessitate sophisticated credit risk measurement.

Purpose of the Study:

  • To develop an improved Bayesian framework for estimating credit risk and associated uncertainties.
  • To enhance the accuracy of default probability and transition probability predictions.
  • To account for model uncertainty and expert accuracy by integrating historical performance data.

Main Methods:

  • Utilizes a Bayesian framework drawing techniques from physical sciences and engineering.
  • Incorporates estimates from multiple rating agencies, weighting them by their historical accuracy.
  • Applies the methodology to assess default probability and transition probabilities, considering expert performance data.

Main Results:

  • The proposed Bayesian methodology yields more accurate default probability estimates than individual rating agency models.
  • The approach effectively incorporates historical accuracy of expert assessments into future risk predictions.
  • Demonstrates robustness in accounting for significant market events, such as the 2008 global banking crisis.

Conclusions:

  • The Bayesian framework offers a superior method for credit risk assessment, improving accuracy and reliability.
  • Integrating historical expert accuracy within a Bayesian structure enhances predictive power for default and transition probabilities.
  • The methodology provides a more resilient approach to credit risk modeling, adaptable to market shocks.