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
|December 4, 2023
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.
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.
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