相关实验视频

Updated: Jul 12, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

206

编码贝叶斯神经网络的隐藏后部,用于不确定性量化

Gianni Franchi, Andrei Bursuc, Emanuel Aldea

    IEEE transactions on pattern analysis and machine intelligence
    |October 31, 2023
    PubMed
    概括

    潜后贝叶斯神经网络 (LP-BNNs) 为深度学习模型提供了高效的解决方案,提高了稳定性和不确定性估计,而无需高额计算成本. 这种方法使得复杂的计算机视觉任务可进行可扩展的贝叶斯深度学习.

    更多相关视频

    Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans09:23

    Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

    Published on: August 16, 2017

    8.1K
    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements10:22

    Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

    Published on: September 7, 2019

    8.3K

    相关概念视频

    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.
    570
    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...
    4.1K
    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...
    704
    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...
    534
    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...
    5.8K
    Probability Distributions01:32

    Probability Distributions

     The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
    A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
    7.2K