Advances in Sequential Monte Carlo Methods for Complex Bayesian Models

复杂贝叶斯模型的顺序蒙特卡罗方法的进展

• 批准号: DP200102101
• 负责人: Prof Christopher Drovandi
• 金额: $ 27.38万
• 依托单位: Queensland University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-05-12 至 2024-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP200102101
• 关键词: Advances; Sequential; Monte; Carlo; Methods

This project aims to develop efficient statistical algorithms for parameter estimation of complex stochastic models that currently cannot be handled. Parameter estimation is an essential component of mathematical modelling for answering scientific questions and revealing new insights. Current parameter estimation methods can be inefficient and require too much user intervention. This project will develop novel Bayesian algorithms that are optimally automated and efficient by exploiting ever-improving parallel computing devices. The new methods will allow practitioners to process realistic models, enabling new scientific discoveries in a wide range of disciplines such as biology, ecology, agriculture, hydrology and finance.

该项目旨在为当前无法处理的复杂随机模型的参数估计开发有效的统计算法。 参数估计是用于回答科学问题并揭示新见解的数学建模的重要组成部分。当前参数估计方法效率低下，需要过多的用户干预。 该项目将开发新型的贝叶斯算法，这些算法通过利用不断改进的并行计算设备来最佳自动化和有效。 这些新方法将使从业者可以处理现实的模型，从而在生物学，生态学，农业，水文学和金融等广泛学科中实现新的科学发现。