预测和时间序列分析哲学博士

研究主题包括：动态模型和模型结构的核心基础和概念基础的调查，顺序统计分析，时变上下文中的学习，具有多个目标和效用的预测和预测以及贝叶斯方法和统计计算的相关领域各种系列。多年来，杜克统计科学一直是贝叶斯预测，时间序列建模和分析的领先中心，而当前的重点研究领域正在继续发展和增强这一传统。面对动态系统，数据集和相关模型的规模不断扩大，概率预测（aka预测）的基础和概念基础的研究变得越来越重要，这与对大型和复杂动态模型的新建模开发的主要刺激相联系。数据集以及新兴的动态决策分析在理论和应用领域的创新需求，这些动态决
Research themes include: Investigations of core foundations and conceptual underpinnings of dynamic modelling and model structures, sequential statistical analysis, learning in time-varying contexts, forecasting and prediction with multiple goals and utilities, and relevant areas of Bayesian methodology and statistical computation, for time series of all kinds. Duke Statistical Science has been a leading center for Bayesian forecasting, time series modelling and analysis for many years, and current topical research areas of emphasis continue to grow and enhance that tradition. Research in the foundations and conceptual underpinnings of probabilistic forecasting (a.k.a. prediction) are increasingly paramount in the face of escalation of the scales of dynamic systems, data sets and relevant models, this links to major stimuli to new modelling developments for increasingly large and complex dynamic data sets, and to the need for innovation in both theoretical and applied areas of emerging dynamic decision analysis linked to many kinds of coupled forecastingdecision problems.