Methodology and theory for unbiased MCMC.

无偏见 MCMC 的方法和理论。

• 批准号: 2284954
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2284954
• 关键词: Methodology; theory; unbiased; MCMC.

As the power and thermal limits of silicon are being reached, modern computing is moving towards increased parallelism. Fast computation is primarily achieved through the usage of many independent processors, which split up and perform the computation task simultaneously. This poses a challenge for Markov chain Monte Carlo, the gold standard of statistical computing, which is an inherently sequential procedure.A recently-proposed methodology enables principled parallel processing for Markov chain Monte Carlo and offers the potential to overcome this challenge. While straightforward to implement, the method may incur a significant computational overhead, rendering it impracticable unless the number of available processors is in the order of thousands, or even more.This project aims to enhance the practicality of the aforementioned methodology, making it competitive with other methods even when the number of processors is in the tens or hundreds. The focus is on: 1) reducing the computational overhead, either through direct refinements or by applying post-processing techniques, and 2) producing practical guidelines for the optimal performance of the new methodology, through theoretical analyses. The work undertaken in this project will be of use to practitioners and researchers who rely on simulation to draw conclusions from their statistical models, throughout science, technology, engineering, and mathematics.In partnership with University College Dublin (Ireland).

随着硅的功率和热极限的不断逼近，现代计算正朝着增加并行性的方向发展。快速计算主要是通过使用许多独立的处理器来实现的，这些处理器拆分并同时执行计算任务。这对马尔可夫链蒙特卡罗提出了挑战，这是统计计算的黄金标准，它是一个固有的顺序过程。最近提出的一种方法能够对马尔可夫链蒙特卡洛进行原则性并行处理，并提供了克服这一挑战的潜力。虽然实现起来很简单，但该方法可能会产生大量的计算开销，除非可用处理器的数量达到数千甚至更多，否则它是不切实际的。该项目旨在提高上述方法的实用性，使其在处理器数量为数十或数百的情况下也能与其他方法竞争。重点是：1)通过直接改进或应用后处理技术来减少计算开销，2)通过理论分析为新方法的最佳性能提供实用指南。在这个项目中进行的工作将对依靠模拟从统计模型中得出结论的实践者和研究人员在整个科学、技术、工程和数学领域都有帮助。与都柏林大学学院（爱尔兰）合作。