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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/bayesian-statistics-time-series-analysis
课程评论：没有评论
课程名称：贝叶斯统计：时间序列分析 课程概述：本课程是为现职和有志于成为数据科学家和统计学家的学员设计的。这是介绍贝叶斯统计基础知识的四门课程系列的第四门课程，前面的课程包括《贝叶斯统计：从概念到数据分析》、《技术与模型》和《混合模型》。时间序列分析关注于建模一系列时间相关变量之间的依赖关系。为了顺利完成本课程，学员需具备基于微积分的概率知识、最大似然估计原理以及贝叶斯推断的相关知识。学员将学习如何构建描述时间依赖性的模型，并如何对这些模型进行贝叶斯推断与预测。课程将使用开放源代码的免费软件R和样本数据库进行实际应用。讲师Raquel Prado将从时间依赖数据建模的基本概念开始，带领学员实现特定类别模型的实施。 课程大纲： - 第1周：时间序列与AR(1)过程介绍 - 本模块定义了平稳时间序列过程、自相关函数及一阶自回归过程（AR(1)）。还讨论了AR(1)中的参数估计、最大似然估计和贝叶斯推断。 - 第2周：AR(p)过程 - 本模块将第1周学习的AR(1)过程的概念扩展到一般的AR(p)情况，并讨论了AR(p)中的最大似然估计和贝叶斯后验推断。 - 第3周：正态动态线性模型，第一部分 - 本模块定义了正态动态线性模型(NDLMs)，并通过多个实例进行说明。介绍了基于超叠加原理的预测函数构建模型的方法，讨论了已知观测方差和已知系统协方差矩阵情况下NDLM的贝叶斯过滤、平滑和预测方法。 - 第4周：正态动态线性模型，第二部分 - （模块具体内容待补充） - 第5周：最终项目 - 在这个最终项目中，学员将使用正态动态线性模型来分析从Google趋势下载的时间序列数据集。
Name:Week 1: Introduction to time series and the AR(1) process
Description:This module defines stationary time series processes, the autocorrelation function and the autoregressive process of order one or AR(1). Parameter estimation via maximum likelihood and Bayesian inference in the AR(1) are also discussed.
Name:Week 2: The AR(p) process
Description:This module extends the concepts learned in Week 1 about the AR(1) process to the general case of the AR(p). Maximum likelihood estimation and Bayesian posterior inference in the AR(p) are discussed.
Name:Week 3: Normal dynamic linear models, Part I
Description:Normal Dynamic Linear Models (NDLMs) are defined and illustrated in this module using several examples. Model building based on the forecast function via the superposition principle is explained. Methods for Bayesian filtering, smoothing and forecasting for NDLMs in the case of known observational variances and known system covariance matrices are discussed and illustrated.
Name:Week 4: Normal dynamic linear models, Part II
Description:
Name:Week 5: Final Project
Description:In this final project you will use normal dynamic linear models to analyze a time series dataset downloaded from Google trend.
This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, Techniques and Models, and Mixture models. Time series analysis is concerned with modeling the dependency among elements of a sequence of temporally related variables. To succeed in this course, you should be familiar with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference. You will learn how to build models that can describe temporal dependencies and how to perform Bayesian inference and forecasting for the models. You will apply what you've learned with the open-source, freely available software R with sample databases. Your instructor Raquel Prado will take you from basic concepts for modeling temporally dependent data to implementation of specific classes of models
