Computational methodologies for state-space models: A Big-Data challenge

数据的现成可用性要求改进模型拟合方法。在这个项目中，我们将探索贝叶斯方法来处理大量的状态空间模型，特别关注模型设计，以捕获包含在大量数据中的信息。高维数和大样本量给模型选择和推理带来了严峻的挑战。事实上，模型的组成部分，如似然本身和状态转换密度，通常是难以处理的上述条件下，基于贝叶斯因子的常规方法，例如，不能实现。在这个项目中，将探索和开发在贝叶斯背景下处理大数据问题的新方法，目的是克服建模和当前计算的局限性。除了在模拟数据支持下进行方法学研究外，该项目还将涉及实际应用，特别是在金融和生态领域。

The ready availability of data requires improved methods for model fitting. Within this project, we will explore Bayesian approaches to handle the large class of state-space models, with a particular focus on model design, to capture the information contained in large amounts of data. High dimensionality and large sample sizes pose serious challenges in model selection and inference. In fact, model components such as the likelihood itself and the state transformation density, are usually intractable under the above conditions and regular approaches based on Bayes factors, for example, cannot be implemented. In this project, new approaches to deal with Big-Data problems within the Bayesian context will be explored and developed with the aim of overcoming modelling and current computational limitations. Besides methodological investigations, supported by simulated data, the project will also involve practical applications, in particular to the areas of finance and ecology.