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Bayesian analyses offer a robust framework for integrating data from multiple sources to better inform population-level estimates of disease prevalence. This methodological approach is particularly suited to instances where data from observational studies is linked to administrative health records, with the capacity to advance our understanding of psychiatric disorders. The objective of our paper was to provide an introductory overview and tutorial on Bayesian analysis for primary observational studies in mental health research. We provided: (i) an overview of Bayesian statistics, (ii) the utility of Bayesian methods for psychiatric epidemiology, (iii) a tutorial example of a Bayesian approach to estimating the prevalence of mood and/or anxiety disorders in observational research, and (iv) suggestions for reporting Bayesian analyses in health research.贝叶斯分析（Bayesian analysis）可为整合多源数据以优化疾病患病率的人群水平估计提供稳健的分析框架。该方法论框架尤其适用于观察性研究数据与行政卫生记录联动的场景，能够助力深化我们对精神障碍的认知。本研究的核心目标是为精神卫生领域的原创性观察性研究提供贝叶斯分析的入门综述与实操教程。本文具体涵盖以下内容：(i) 贝叶斯统计学（Bayesian statistics）综述；(ii) 贝叶斯方法在精神流行病学（psychiatric epidemiology）中的应用价值；(iii) 观察性研究中心境障碍及/或焦虑障碍患病率估计的贝叶斯方法实操案例；(iv) 卫生研究中贝叶斯分析的报告规范建议。