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相关概念视频

Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

529
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...
529
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

699
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...
699
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
43
Uncertainty: Overview00:59

Uncertainty: Overview

563
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.
563
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

448
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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对于无氧消化模型的贝叶斯不确定性量化.

Antoine Picard-Weibel1, Gabriel Capson-Tojo2, Benjamin Guedj3

  • 1SUEZ, CIRSEE, 38 rue du Président Wilson, 78230 Le Pecq, France; Laboratoire Paul Painlevé, Univ. de Lille Cité Scientifique, F-59655 Villeneuve d'Ascq, France; MODAL, Inria 40 avenue Halley, 59650 Villeneuve d'Ascq, France.

Bioresource technology
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概括

一种新的贝叶斯方法,VarBUQ,量化了无氧消化模型中的不确定性. 它平衡了灵活性和计算成本,通过避免对预测过度自信而优于其他方法.

关键词:
生物化学反应网络 生物化学反应网络计算模型 计算模型信任地区是信任地区.预测能力的预测能力.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 环境工程环境工程
  • 统计建模 统计建模

背景情况:

  • 不确定性量化对于生物学的可靠计算模型至关重要.
  • 无氧消化模型被广泛使用,但需要强大的不确定性评估.
  • 现有的不确定性量化方法可能是计算上昂贵或过于自信的.

研究的目的:

  • 介绍一种新的概括贝叶斯程序 (VarBUQ) 用于不确定性量化.
  • 使用合成数据对VarBUQ的性能进行评估,并与已知的方法进行比较.
  • 为生物模型提供一个计算效率高的贝叶斯方法.

主要方法:

  • 开发了一个称为VarBUQ的泛化贝叶斯程序.
  • 将VarBUQ与费舍尔的信息,引导和比尔的标准进行了对比.
  • 利用合成数据对不确定性量化方法进行比较分析.

主要成果:

  • VarBUQ在模型适配和信心估计之间取得了有利的平衡.
  • 传统方法 (费舍尔的信息,引导,比尔的标准) 被发现过于自信.
  • 通过精心构建的先前分布的诱导偏差,VarBUQ的性能得到了提高.

结论:

  • 在无氧消化模型中,VarBUQ提供了一种计算效率高,可靠的方法来量化无确定性.
  • 该研究主张在生物建模中更加关注不确定性.
  • 发布了一个Python包"aduq",以支持VarBUQ.Q的实现.