Efficient Bayesian analysis for complex models

在各种科学和实际应用中，用有限数量的观测值拟合复杂模型变得越来越必要。例如，医学面临着利用遗传筛查工具开发疾病预测模型的挑战，经济学家必须建立一个预测金融和经济指数的模型。传统的统计方法由于涉及的变量众多而无法达到要求的结果。标准方法必须选择数据的一个子集，忽略绝大多数变量，以管理信息并实现结果。我的研究重点是为这些数据集开发校准良好且计算效率高的统计方法。在我以前的工作中，我已经展示了管理大型数据集（例如，具有数千个变量）的机制，并构建了有效的软件工具来实现贝叶斯模型来分析这些数据集。我目前的研究将致力于开发更有效的贝叶斯统计方法，并试图将其应用于各种各样的真实的问题。

Fitting complex models with a limited number of observations is becoming increasingly necessary in various scientific and practical applications. For example, medical science faces challenges in developing disease predictive models using genetic screening tools, and economists must build a model for forecasting financial and economic indices. Traditional statistical methods fail to achieve required results due to the large number of variables involved. Standard methods must select a subset of data, ignoring the vast majority of variables, in order to manage information and achieve results. My research focuses on developing well-calibrated and computationally efficient statistical methodologies for such data sets. In my previous work, I have demonstrated mechanisms to manage large data sets (e.g. with thousands of variables) and constructed efficient software tools to implement Bayesian models to analyze such data sets. My current research will be directed towards developing more efficient Bayesian statistical methodologies and attempting to apply them to a wide variety of real problems.