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Related Concept Videos

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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...
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Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Uncertainty: Overview00:59

Uncertainty: Overview

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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.
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Confidence Intervals01:21

Confidence Intervals

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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...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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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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Related Experiment Video

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Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
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Max-confidence boosting with uncertainty for visual tracking.

Wen Guo, Liangliang Cao, Tony X Han

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 14, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces max-confidence boosting (MCB), a novel visual tracking method that handles changing environments by modeling uncertainty. MCB effectively alleviates visual tracking drift issues.

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    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Visual tracking faces challenges due to dynamic changes in foreground and background appearances.
    • Existing tracking-by-classification methods often neglect visual modeling ambiguities and prior information.

    Purpose of the Study:

    • To present a novel visual tracking method, max-confidence boosting (MCB), designed to address the challenges of dynamic environments.
    • To explore a new approach for online updating in the presence of ambiguous visual phenomena.

    Main Methods:

    • The proposed max-confidence boosting (MCB) framework models uncertainty using indeterministic labels.
    • MCB updates classification models by incorporating information from previous and current frames, allowing for ambiguity.

    Main Results:

    • Experimental results on challenging video sequences demonstrate the effectiveness of the MCB tracker.
    • The MCB tracker shows superior performance compared to several state-of-the-art tracking-by-classification methods.

    Conclusions:

    • The MCB method offers a robust solution for visual tracking in complex, time-varying environments.
    • MCB successfully alleviates the drift problem inherent in many visual tracking algorithms.