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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/bayesian-statistics-capstone
课程评论：没有评论
课程总结：贝叶斯统计：毕业设计 本课程是加州大学圣克鲁兹分校贝叶斯统计专业化的毕业项目，旨在为学习者提供一个展示其在贝叶斯统计领域广泛知识和技能的机会，应用所学知识于真实数据中。课程通过讲座视频和测验回顾贝叶斯统计的基本概念，学习者将进行复杂的数据分析，并撰写关于其方法和结果的报告。 课程大纲如下： 1. **模块一：自回归时间序列模型的贝叶斯共轭分析** - 本模块将介绍自回归(AR)模型的共轭贝叶斯分析。 2. **模块二：模型选择标准** - 在本模块中，我们将介绍一些用于选择AR过程的阶数和混合成分数量的标准，这些将在后续讨论混合AR模型时使用。 3. **模块三：贝叶斯位置混合AR(P)模型** - 本模块将对位置混合AR(P)模型进行贝叶斯分析。 4. **模块四：同行评审数据分析项目** - 在这一模块中，学习者将利用此前所学的知识，对时间序列数据进行混合模型的分析。 通过这些模块，学习者将深入理解贝叶斯统计的理论与应用，为实际数据分析打下坚实的基础。
Part: 1
Title:Bayesian Conjugate Analysis for Autogressive Time Series Models
Description:In this module, we will introduce conjugate Bayesian analysis for the autoregressive (AR) models.
Part: 2
Title:Model Selection Criteria
Description:In this module, we will introduce some criteria that can be used in selecting the order of AR processes and the number of mixing components, which will be used later when we introduce mixture of AR models.
Part: 3
Title:Bayesian location mixture of AR(P) model
Description:In this module, we will perform Bayesian analysis for location mixture of AR(p) models.
Part: 4
Title:Peer-reviewed data analysis project
Description:In this module, we will use everything we have learned up until now to perform a mixture model on time series data.
This is the capstone project for UC Santa Cruz's Bayesian Statistics Specialization. It is an opportunity for you to demonstrate a wide range of skills and knowledge in Bayesian statistics and to apply what you know to real-world data. You will review essential concepts in Bayesian statistics with lecture videos and quizzes, and you will perform a complex data analysis and compose a report on your methods and results.
