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Related Concept Videos

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...
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: 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...
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...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...

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P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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Improving Bayesian credibility intervals for classifier error rates using maximum entropy empirical priors.

Mats G Gustafsson1, Mikael Wallman, Ulrika Wickenberg Bolin

  • 1Uppsala University, Department of Medical Sciences, Academic Hospital, 751 85 Uppsala, Sweden. Mats.Gustafsson@medsci.uu.se

Artificial Intelligence in Medicine
|March 30, 2010
PubMed
Summary
This summary is machine-generated.

Maximum entropy (ME) priors improve Bayesian credibility intervals (CI) for classifier error rates, especially with limited test data. This approach enhances performance estimation in machine learning for biomedical applications.

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Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Biomedical Informatics

Background:

  • Robust performance estimation is crucial for classifiers using patient data.
  • Conventional Bayesian credibility intervals (CI) for error rates can be too wide with small test sets.
  • Uniform priors in conventional CIs do not leverage existing knowledge.

Purpose of the Study:

  • To investigate a maximum entropy (ME) based approach for improved prior knowledge in Bayesian CIs.
  • To demonstrate the relevance of ME priors for biomedical research and clinical practice.
  • To enhance the precision of error rate estimation for classifiers.

Main Methods:

  • Utilized the maximum entropy (ME) principle to derive non-uniform prior density distributions.
  • Employed empirical results from non-overlapping training and testing sets.
  • Applied ME priors to Bayesian credibility interval calculations.

Main Results:

  • ME-based priors demonstrated improvement in Bayesian CIs across diverse datasets.
  • Effectiveness shown on four simulated and two real-world biomedical datasets.
  • Enhanced precision in error rate estimation was observed.

Conclusions:

  • Empirically derived maximum entropy (ME) priors show promise for improving Bayesian CIs.
  • This method offers a more refined approach to estimating unknown classifier error rates.
  • The ME-based approach is relevant for practical applications in biomedical research.