New Developments for Bayesian statistical models and computational methods.

贝叶斯统计分析方法为在存在不确定性的情况下进行推理提供了一种灵活的理论。因此，贝叶斯方法使得在被描述为复杂系统的领域中能够进行科学发现，在这些领域中，建模和计算方法的新发展至关重要。进一步取得成功的重大障碍包括建立和验证模型、处理大数据集和开发有效的计算方法方面的挑战。该项目的主要目的是开发新的贝叶斯建模和计算方法，以广泛应用来应对这些挑战。

Bayesian methods of statistical analysis provide a flexible theory for addressing inference in the presence of uncertainty. Consequently Bayesian methods have enabled scientific discovery in areas characterised as complex systems where new developments in modelling and computational methods have been crucial. Significant barriers to further success involve challenges in formulating and validating models, dealing with large data sets, and developing efficient computational methods. The principal aim of this project is to develop new Bayesian modelling and computational methodology which address these challenges with broad application.