New Bayesian methodology for understanding complex systems using hidden Markov models and expert opinion, environmental, robotics and genomics applications

使用隐马尔可夫模型和专家意见、环境、机器人和基因组学应用来理解复杂系统的新贝叶斯方法

• 批准号: ARC : DP0344114
• 负责人: Distinguished Professor Kerrie Mengersen
• 金额: $ 19.5万
• 依托单位: The University of Newcastle
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2003
• 资助国家: 澳大利亚
• 起止时间: 2003-01-01 至 2005-12-31
• 项目状态: 已结题
• 来源: https://researchdata.edu.au/new-bayesian-methodology-genomics-applications/66505
• 关键词: New; Bayesian; methodology; understanding; complex

New Bayesian methodology for understanding complex systems using hidden Markov models and expert opinion, environmental, robotics and genomics applications. This project aims to merge four areas of intense international interest in describing complex systems: hidden Markov models and mixtures, semi-parametric and nonparametric approaches, true combination of expert opinion with data, and new Bayesian computational methods based on perfect sampling and particle sampling. The project will significantly contribute to statistical methodology and its ability to inform about real-world problems. A strong focus on applications to genomics, robotics and environmental modelling will bring immediate research and monetary benefit for industry. Expected outcomes include enhanced cross-disciplinary and international linkages, publications, industry-funded projects and highly trained graduates.

使用隐马尔可夫模型和专家意见，环境，机器人和基因组学应用来理解复杂系统的新贝叶斯方法。该项目旨在融合国际上对描述复杂系统的四个领域的强烈兴趣：隐马尔可夫模型和混合物，半参数和非参数方法，专家意见与数据的真正结合，以及基于完美采样和粒子采样的新贝叶斯计算方法。该项目将对统计方法及其了解现实世界问题的能力作出重大贡献。对基因组学、机器人技术和环境建模应用的强烈关注将为工业带来直接的研究和经济效益。预期成果包括加强跨学科和国际联系、出版物、行业资助项目和训练有素的毕业生。