Advances in Sequential Monte Carlo Methods for Complex Bayesian Models

本项目旨在为目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。参数估计是回答科学问题和揭示新见解的数学建模的重要组成部分。目前的参数估计方法可能效率低下，并且需要过多的用户干预。该项目将开发新颖的贝叶斯算法，通过利用不断改进的并行计算设备实现最佳自动化和高效。这些新方法将使实践者能够处理现实的模型，从而在生物学、生态学、农业、水文学和金融学等广泛学科中实现新的科学发现。

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.