Using graphical structure to develop an optimizing compiler for the Turing probabilistic language

复杂的统计建模为应对新冠肺炎疫情的医疗和社会应对提供了重要工具。贝叶斯或概率方法的使用使得能够综合来自多个数据源的信息，并在数据分析和决策管道中传播预测不确定性。然而，这种类型的建模是一项困难的工作，需要几个技术领域的专业知识。因此，医学研究的进步受到能够进行建模的人员短缺的阻碍，这限制了可以调查的重要研究问题的数量。提高复杂统计建模能力的一种方法是开发工具，使建模更容易进行。用于此目的的特别有吸引力的工具是概率编程语言(PPL)。它们被设计成允许非专家使用正式的计算机语言来指定复杂的通用模型。可以使用软件工具从其PPL描述编译用于指定模型的推理算法。然后，可以运行推理算法，以自动方式有效地拟合模型。最古老和最流行的PPL之一是Bugs语言。该语言及其支持软件旨在利用图形模型及其条件独立性属性。最近，已经开发了许多新的ppls，它们能够使用可微编程，并应用比bugs软件更强大的推理算法。一个特别有前途的新PPL是图灵，它使用Julia语言作为其计算核心。我们建议将Bugs软件的易用性与基于Julia的核心图灵相结合，为统计学家和数据科学家提供一个用于复杂建模的新的强大工具。来自Bugs软件的条件独立性论证将被用来提高图灵软件的效率，并允许其推理算法以被证明正确的方式在多核计算机上运行。更具体地说，Bugs语言的语法将使用BNF进行形式化。类型系统将被用来将用Bugs语言编写的程序限制为统计上有意义的模型。将建立模型的中级表示，以允许编译优化的推理算法。Bugs和Turing是两个研究项目，最初分别由临床医学院的MRC生物统计股和理工学院的机器学习小组开发。拟议的合作项目允许汇集双方(MRC生物统计股和机器学习小组)的最佳专业知识，并应用最先进的工程方法来促进医学研究。

Complex statistical modelling has provided important tools in the medical and social response to the COVID-19 pandemic. The use of Bayesian or probabilistic approaches has allowed synthesising information from multiple data sources and propagating prediction uncertainty within the data analysis and decision-making pipelines. However, this type of modelling is a difficult undertaking, requiring expertise in several technical areas. As a result, advances in medical research are hindered by the shortage of people able to carry out modelling, which limits the number of research questions of importance that could be investigated. One way to increase capacity in complex statistical modelling is by developing tools to make the modelling easier to carry out. Particularly attractive tools for this purpose are probabilistic programming languages (PPL). They are designed to allow non-experts to specify complex generic models using a formal computer language. Software tools can be used to compile inference algorithms for the specified model from its PPL description. The inference algorithms can then be run to fit the model efficiently in an automated manner. One of the oldest and most popular PPL is the BUGS language. This language and its supporting software were designed to exploit graphical models and their conditional independence properties. More recently, many new PPLs have been developed that are able to use differentiable programming and apply more powerful inference algorithms than the BUGS software could. One particularly promising new PPL is Turing, which uses the Julia language as its computational core. We propose combining the ease of use of the BUGS software with the Julia based core of Turing to give statisticians and data scientists a new powerful tool for complex modelling. The conditional independence arguments from the BUGS software will be used to increase the efficiency of the Turing software and allow its inference algorithm to run on multicore computers in a provably correct way. More concretely, the grammar for the BUGS language would be formalized using BNF. A type system would be used to restrict programs written in the BUGS language to statistically meaningful models. An intermediate level representation of the model would be built to allow optimized inference algorithms to be compiled. BUGS and Turing are two research projects initially developed by the MRC Biostatistics Unit within the School of Clinical Medicine and the Machine Learning Group within the School of Technology, respectively. The proposed collaborative project allows bringing together the best expertise from both sides (MRC Biostatistics Unit and Machine Learning Group) and applying state-of-the-art engineering approaches to advance medical research.