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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

3.0K
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. 
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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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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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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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...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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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...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Related Experiment Videos

Ensemble Stochastic Configuration Networks for Estimating Prediction Intervals: A Simultaneous Robust Training

Jun Lu, Jinliang Ding, Xuewu Dai

    IEEE Transactions on Neural Networks and Learning Systems
    |February 20, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a robust method using ensemble stochastic configuration networks (SCNs) and bootstrap to improve prediction intervals (PIs) for industrial processes. The approach enhances accuracy and quantifies uncertainty in noisy data.

    Related Experiment Videos

    Area of Science:

    • Industrial Process Monitoring
    • Machine Learning for Engineering
    • Data Uncertainty Quantification

    Background:

    • Industrial data often contains noise and outliers, challenging accurate variable prediction.
    • Prediction Intervals (PIs) are crucial for quantifying uncertainty in point predictions.
    • Existing methods may lack robustness or computational efficiency in handling noisy industrial data.

    Purpose of the Study:

    • To develop an improved method for estimating PIs in industrial processes.
    • To enhance prediction accuracy and uncertainty quantification for noisy datasets.
    • To ensure modeling stability and computational efficiency in predictions.

    Main Methods:

    • Ensemble Stochastic Configuration Networks (SCNs) combined with the bootstrap method for PI estimation.
    • A simultaneous robust training method for ensemble SCNs using Bayesian ridge regression and M-estimate.
    • Expectation-maximization (EM) algorithm for estimating hyperparameters and optimizing PIs.

    Main Results:

    • The proposed ensemble SCNs approach demonstrated improved PI quality and prediction accuracy.
    • The robust training method enhanced the model's resilience to noise and outliers.
    • The EM algorithm optimized hyperparameters, leading to superior PI performance.

    Conclusions:

    • The developed ensemble SCNs with robust training and EM optimization provide accurate and reliable PIs for industrial processes.
    • This method effectively addresses challenges posed by noisy and outlier-contaminated industrial data.
    • The approach offers a stable, efficient, and robust solution for uncertainty quantification in industrial predictions.