Innovating and Validating Scalable Monte Carlo Methods

创新和验证可扩展的蒙特卡罗方法

• 批准号: DE240101190
• 负责人: Dr Leah South
• 金额: $ 31.27万
• 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: 2024
• 资助国家: 澳大利亚
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE240101190
• 关键词: Innovating; Validating; Scalable; Monte; Carlo

This project aims to develop innovative scalable Monte Carlo methods for statistical analysis in the presence of big data or complex mathematical models. Existing approaches to scalable Monte Carlo are only approximate, and their inaccuracies are difficult to quantify. This can have a detrimental impact on data-based decision making. The expected outcomes of this project are scalable Monte Carlo methods that are more accurate, fast and capable of quantifying inaccuracies. Scientists and decision-makers will benefit from the ability to obtain timely, reliable insights for challenging applications.

该项目旨在在存在大数据或复杂数学模型的情况下开发创新的可扩展蒙特卡洛方法来进行统计分析。现有的可扩展蒙特卡洛的方法仅是近似的，它们的不准确性很难量化。这可能会对基于数据的决策产生不利影响。该项目的预期结果是可扩展的蒙特卡洛方法，它们更准确，快速且能够量化不准确性。科学家和决策者将从获得及时，可靠的见解的能力中受益。