Tractable Bayesian algorithms for intractable Bayesian problems

用于解决棘手贝叶斯问题的易处理贝叶斯算法

• 批准号: DE160100741
• 负责人: Prof Christopher Drovandi
• 金额: $ 26.78万
• 依托单位: Queensland University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-03-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE160100741
• 关键词: Tractable; Bayesian; algorithms; intractable; problems

This project seeks to develop computationally efficient and scalable Bayesian algorithms to estimate the parameters of complex models and ensure inferences drawn from the models can be trusted. Bayesian parameter estimation and model validation procedures are currently computationally intractable for many complex models of interest in science and technology. These include biological processes such as the efficacy of heart disease, wound healing and skin cancer treatments. Potential outcomes of the project include new algorithms to significantly economise computations and improved understanding of the mechanisms of experimental data generation. Improved models of wound healing, skin cancer growth and heart physiology supported by these algorithms could improve population health.

该项目旨在开发计算高效且可扩展的贝叶斯算法，以估算复杂模型的参数并确保可以信任从模型中得出的推断。贝叶斯参数估计和模型验证程序目前在许多复杂的科学和技术兴趣模型上都在计算上是棘手的。这些包括生物学过程，例如心脏病的功效，伤口愈合和皮肤癌治疗。该项目的潜在结果包括新算法，以显着节省计算，并提高对实验数据生成机制的理解。这些算法支持的伤口愈合，皮肤癌生长和心脏生理学的改善模型可以改善人口健康。