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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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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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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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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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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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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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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: Feb 24, 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

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Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction.

Hossein Soleimani, James Hensman, Suchi Saria

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 26, 2017
    PubMed
    Summary

    This study introduces a novel joint model using sparse Gaussian processes for event prediction from complex time series data. The new method improves accuracy and scalability, outperforming existing techniques in real-world scenarios.

    Related Experiment Videos

    Last Updated: Feb 24, 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

    2.7K

    Area of Science:

    • Machine Learning
    • Statistical Modeling
    • Biostatistics

    Background:

    • Irregularly sampled multivariate time series data present challenges for event prediction due to missing data and noise.
    • Existing imputation methods do not adequately address uncertainty from missingness.
    • Current joint modeling techniques often rely on strong parametric assumptions and struggle with scalability.

    Purpose of the Study:

    • To develop a flexible and scalable joint model for event prediction from longitudinal data.
    • To address limitations of existing methods in handling missing data, noise, and scalability.
    • To derive an optimal event prediction policy that balances detection costs and accuracy.

    Main Methods:

    • Developed a joint model using sparse multiple-output Gaussian processes capable of handling non-Gaussian noise and large datasets.
    • Derived an optimal policy for event prediction based on estimated event occurrence probabilities.
    • The policy incorporates a confidence criterion to avoid decisions with insufficient evidence.

    Main Results:

    • The proposed Gaussian process-based joint model demonstrated flexibility and scalability, outperforming existing methods.
    • The model effectively handles complex data structures, including non-Gaussian noise.
    • Experimental results on a large dataset confirmed significant improvements in event prediction accuracy.

    Conclusions:

    • The novel joint modeling framework offers a robust and scalable solution for event prediction from challenging multivariate time series data.
    • The derived optimal policy enhances decision-making by incorporating uncertainty and cost-benefit trade-offs.
    • This approach represents a significant advancement in accurately predicting events from longitudinal data.