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Regul Toxicol Pharmacol. 2023 Jun;141:105389. doi: 10.1016/j.yrtph.2023.105389. Epub 2023 Apr 13.
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ToxicR: A computational platform in R for computational toxicology and dose-response analyses.ToxicR:R语言中用于计算毒理学和剂量反应分析的计算平台。
Comput Toxicol. 2023 Feb;25. doi: 10.1016/j.comtox.2022.100259. Epub 2022 Dec 27.
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Stan: A Probabilistic Programming Language.斯坦:一种概率编程语言。
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The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App.贝叶斯统计中先验敏感性分析的重要性:使用交互式Shiny应用程序的演示
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Semiparametric Bayesian joint modeling of a binary and continuous outcome with applications in toxicological risk assessment.二元和连续结果的半参数贝叶斯联合建模及其在毒理学风险评估中的应用
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Bayesian latent variable models for mixed discrete outcomes.用于混合离散结果的贝叶斯潜在变量模型。
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贝叶斯统计概念及其在啮齿动物毒理学研究中的应用实例。

Bayesian statistical concepts with examples from rodent toxicology studies.

机构信息

Social & Scientific Systems, Inc., a DLH Holdings Corp Company, Durham, USA.

Division of Intramural Research, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, USA.

出版信息

Lab Anim. 2024 Oct;58(5):470-475. doi: 10.1177/00236772241262829. Epub 2024 Sep 20.

DOI:10.1177/00236772241262829
PMID:39301794
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11559269/
Abstract

The theory and practice of statistics comprises two main schools of thought: frequentist statistics and Bayesian statistics. Frequentist methods are most commonly used to analyze animal-based laboratory data, while Bayesian statistical methods have been implemented less widely and may be relatively unfamiliar to practitioners in experimental science. This paper provides a high-level overview of Bayesian statistics and how they compare with frequentist methods. Using examples in rodent toxicity research, we argue that Bayesian methods have much to offer laboratory animal researchers. We advocate for increased attention to and adoption of Bayesian methods in laboratory animal research. Bayesian statistical theory, methods, software, and education have advanced significantly in the last 30 years, making these tools more accessible than ever.

摘要

统计学的理论和实践主要包括两个学派

频率统计学和贝叶斯统计学。频率统计学方法最常用于分析基于动物的实验室数据,而贝叶斯统计方法的应用则相对较少,可能对实验科学的从业者来说相对陌生。本文提供了贝叶斯统计学的高级概述,以及它们与频率统计学方法的比较。我们使用啮齿动物毒性研究中的例子,认为贝叶斯方法为实验室动物研究人员提供了很多优势。我们主张在实验室动物研究中更多地关注和采用贝叶斯方法。在过去的 30 年中,贝叶斯统计理论、方法、软件和教育都取得了重大进展,使得这些工具比以往任何时候都更容易获得。