Bayesian Time Series and Dynamic Models

贝叶斯时间序列与动态模型 摘要 这项研究涉及开发新的统计模型， 分析和预测时间序列的方法和计算算法。调查将包括时间序列的贝叶斯状态空间建模的各个方面，引入非平稳时间序列模型的新模型，结构化潜在过程分析和时间序列分解的方法，多个时间序列分析的新模型和计算方法，包括动态潜在因素模型，动态贝叶斯分层模型，以及非高斯和非线性模型。 部分研究涉及调查的战略， 处理高维时间序列数据集和时间序列数据的约简与汇总问题。随机模拟 和计算机算法是成分，并将导致新的 在动态建模和时间序列分析的各种问题的模拟为基础的贝叶斯分析的计算工具。 相关的研究领域将包括时间序列模型的不确定性，评估和事先规范，污染结构和数据清洗问题，以及模型 涉及统计混合结构。 这些项目代表了时间序列建模和分析方面的进展，并对统计科学的其他领域作出了贡献，这将引起广泛的兴趣并具有实际意义， 各学科的科学家和研究人员以及 统计学家。 该项目产生的新的统计方法扩展了当前的适用范围 系列分析工具，以更复杂的，非线性和多变量的问题。该研究的一个重要组成部分涉及计算密集型方法，以适应和探索应用程序中的数据模型。这项研究的动机，部分是由现有的问题，统计建模和推理在当前的应用领域在几个领域，特别是包括在神经科学项目，在地质学驱动的全球环境变化的问题，在金融建模和预测，以及在生物医学工程和信号处理。

Bayesian Time Series and Dynamic Models Mike West Abstract This research involves development of new statistical models, methods and computational algorithms for analysis and forecasting of time series. The investigations will include various aspects of Bayesian state-space modelling for time series, introducing new models for non-stationary time series models, methodology for structured latent process analysis and time series decompositions, new models and computational methods for multiple time series analysis including dynamic latent factor models, dynamic Bayesian hierarchical models, and non-Gaussian and non-linear models. Part of the research involves investigation of strategies for dealing with high-dimensional time series data sets and issues of time series data reduction and summary. Stochastic simulation and computer algorithms are ingredients, and will lead to new computational tools for simulation-based Bayesian analyses in various problems of dynamic modelling and time series analysis. Related areas of the research will include time series model uncertainty, assessment and prior specifications, problems of contamination structures and data cleaning, and models involving statistical mixture structure. These projects represent advances in time series modelling and analysis, and contributions to other areas of statistical science, that will be of broad interest and practical relevance to scientists and researchers in various disciplines as well as to statisticians. The new statistical methods resulting from this project extend the scope of applicability of current time series analysis tools to more complex, non-linear and multivariate problems. A significant component of the research involves computationally intensive methods to fit and explore models to data in applications. The research is motivated, in part, by existing problems of statistical modelling and inference in current application areas in several fields, specifically inclu ding projects in the neurosciences, in geology driven by issues of global environmental change, in financial modelling and forecasting, and in biomedical engineering and signal processing.