Advanced Bayesian Computation for Cross-Disciplinary Research

我们生活在一个数据丰富的时代。快速的技术进步，如互联网，使收集、存储和共享大量信息变得比以往任何时候都更容易。海量数据的可获得性对社会、商业和科学产生了重大影响，数据在科学中发挥着特别重要的作用。数据是你从实验中得到的，数据是你用来检验科学理论的。近年来，科学界收集和产生的数据量大幅增长。我们需要更好的工具来模拟这些数据，以便我们能够理解和测试理论并做出科学预测。我们的建议侧重于用于模拟数据的高级统计工具。重要的是，这些模型是基于概率和统计的，因为任何真实世界现象的模型都必须代表我们从不完全信息和噪声测量中获得的不确定性。概率论为表达模型中的不确定性提供了一种连贯的数学语言。我们的建议发展了基于贝叶斯统计的模型，这种统计在20世纪以前一直被称为“逆概率”，并指的是应用概率理论从可观测数据中学习未知量。贝叶斯统计还可以用来比较给定数据的多个模型(即假设)，从而在科学假设检验中发挥基础作用。我们将开发新的贝叶斯建模计算工具，确保模型足够灵活，足以捕捉真实世界现象的复杂性，并具有足够的可扩展性，可以处理非常大的数据集。我们还将开发新的方法来决定收集哪些数据和进行哪些实验，这可以大大降低科学研究的成本。我们将利用计算机硬件的最新进展，以大规模并行图形处理单元(GPU)的形式加快科学数据的建模。这一建议真正是跨学科的，因为我们不关注单一的科学学科。事实上，我们已经组建了一个团队，他们的专业知识横跨物理、生物和社会科学的贝叶斯建模。我们将创建建模工具，以便更好地对天空进行天文测量，以便我们能够了解宇宙的组成；我们将创建分析基因和蛋白质数据的工具，以便我们能够更好地了解生物现象和设计药物疗法；我们将开发强大的经济和金融数据建模和预测方法，这些方法有望降低金融市场的风险。令人惊讶的是，这些不同的科学领域-天文学、生物学和经济学--可以通过一套统一的计算和统计建模工具聚集在一起。我们的进展不仅将使这些领域受益，还将使基于数据密集型建模的许多其他科学领域受益。

We live in an era of abundant data. Rapid technological advances, such as the internet, have made it possible to collect, store and share large amounts of information more easily than ever before. The availability of large amounts of data has had a major impact on society, commerce, and the sciences.Data plays a particularly important role in the sciences. Data is what you get from conducting experiments, and data is what you use to test scientific theories. In recent years, the amount of data collected and generated in the sciences has grown tremendously. We need better tools to model this data, so that we can understand and test theories and make scientific predictions.Our proposal focuses on advanced statistical tools for modelling data. It is important that the models are based on probability and statistics, because any model of real world phenomena has to represent the uncertainty we have from incomplete information and noisy measurements. Probability theory provides a coherent mathematical language for expressing uncertainty in models. Our proposal develops models based on Bayesian statistics, which used to be called``inverse probability'' until the 20th century, and refers to the application of probability theory to learn unknown quantities from observable data. Bayesian statistics can also be used to compare multiple models (i.e. hypotheses) given the data, and thus can play a fundamental role in scientific hypothesis testing.We will develop new computational tools for Bayesian modelling, ensuring that the models are flexible enough to capture the complexity of real-world phenomena and scalable enough to deal with very large data sets. We will also develop new methods for deciding which data to collect and which experiments to perform, which can greatly reduce the cost of scientific inquiry. We will make use of the latest advances in computer hardware, in the form of massively parallel graphics processing units (GPUs) to speed up modelling of scientific data. This proposal is truly cross-disciplinary in that we do not focus on a single scientific discipline. In fact, we have assembled a team whose expertise spans Bayesian modelling across the physical, biological and social sciences. We will create modelling tools for better astronomical surveying of the skies so that we can understand the composition of our universe;we will create tools for analysing gene and protein data to so that we can better understand biological phenomena and design drug therapies; and we will develop powerful methods for modelling and predicting economic and financial data which will hopefully reduce risk in financial markets. Surprisingly, these diverse areas of the sciences---astronomy, biology and economics---can come together through a unified set of computational and statistical modelling tools. Our advances will benefit not just these areas but many other areas of science based on data-intensive modelling.