Sophisticated mathematical and computational tools have become widespread and important in veterinary pharmacology. Although the theoretical basis and practical applications of these have been widely explored in the literature, statistical inference in the context of these models has received less attention. Optimization methods, often with frequentist statistical inference, have been predominant. In contrast, Bayesian statistics have not been widely applied, but offer both practical utility and arguably greater interpretability. Veterinary pharmacology applications are generally well supported by relevant prior information, from either existing substantive knowledge, or an understanding of study and model design. This facilitates practical implementation of Bayesian analyses that can take advantage of this knowledge. This essay will explore the specification of Bayesian models relevant to veterinary pharmacology, including demonstration of prior selection, and illustrate the capability of these models to generate practically useful statistics, including uncertainty statements, that are difficult or impossible to obtain otherwise. Case studies using simulated data will describe applications in clinical trials, pharmacodynamics, and pharmacokinetics, all including multilevel modeling. This content may serve as a suitable starting point for researchers in veterinary pharmacology and related disciplines considering Bayesian estimation for their applied work.

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兽医药理学中的贝叶斯估计：概念和实践介绍

复杂的数学和计算工具在兽医药理学中已变得广泛且重要。尽管这些模型的理论基础和实际应用已在文献中得到广泛探讨，但这些模型背景下的统计推断却很少受到关注。优化方法（通常采用频率统计推断）一直占据主导地位。相比之下，贝叶斯统计尚未得到广泛应用，但提供了实用性和可以说更大的可解释性。兽医药理学应用通常得到相关先验信息的良好支持，这些信息要么来自现有的实质性知识，要么来自对研究和模型设计的理解。这有利于利用这些知识的贝叶斯分析的实际实施。本文将探讨与兽医药理学相关的贝叶斯模型的规范，包括先前选择的演示，并说明这些模型生成实际有用的统计数据的能力，包括不确定性陈述，而这些统计数据很难或不可能获得。使用模拟数据的案例研究将描述临床试验、药效学和药代动力学中的应用，所有这些都包括多级建模。本内容可以作为兽医药理学和相关学科的研究人员在其应用工作中考虑贝叶斯估计的合适起点。