The validation of approximate Bayesian computation

近似贝叶斯计算的验证

• 批准号: DP170100729
• 负责人: Prof Gael Martin
• 金额: $ 27.42万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2017
• 资助国家: 澳大利亚
• 起止时间: 2017-02-01 至 2021-12-17
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP170100729
• 关键词: validation; approximate; Bayesian; computation

This project aims to establish the theoretical validity of approximate Bayesian computation (ABC) and to develop diagnostic methods for assessing its reliability in empirical applications. Given the increased complexity of modern statistical models, new ways of conducting statistical inference are needed. Approximate Bayesian computation is a new statistical tool. This project expects its findings will be useful in all fields where complex phenomena feature and approximate methods are the only feasible way of understanding those phenomena.

该项目旨在建立近似贝叶斯计算（ABC）的理论有效性，并开发用于评估其在实证应用中的可靠性的诊断方法。鉴于现代统计模型的复杂性不断增加，需要新的统计推断方法。近似贝叶斯计算是一种新的统计工具。该项目预计其研究结果将在复杂现象特征和近似方法是理解这些现象的唯一可行方法的所有领域中发挥作用。