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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/mixture-models
课程评论：没有评论
课程名称：贝叶斯统计：混合模型 课程概述：本课程《贝叶斯统计：混合模型》将带您深入了解一种重要的统计模型类别。课程分为五个模块，每个模块包含讲座视频、小测验、背景阅读材料、讨论提示和一个或多个同行评审的作业。统计学最好通过实际操作学习，而不仅仅是观看视频，因此课程的结构旨在促进通过应用学习。某些练习需要使用R语言，这是一种免费提供的统计软件包。虽然课程提供了简要的R语言教程，但我们鼓励您利用其他在线资源进一步学习R语言，如果您对此感兴趣。 本课程为中级课程，旨在成为加州大学圣克鲁兹分校贝叶斯统计系列的第三门课程，前两门课程分别是Herbie Lee的《贝叶斯统计：从概念到数据分析》和Matthew Heiner的《贝叶斯统计：技术与模型》。为成功完成本课程，您需要具备一定的微积分基础概率知识、最大似然估计原理和贝叶斯估计方面的舒适度。 课程大纲： 1. 混合模型基本概念：定义混合模型，讨论其特性，并开发混合模型随机样本的似然函数，这将成为统计学习的基础。 2. 混合模型的最大似然估计。 3. 混合模型的贝叶斯估计。 4. 混合模型的应用。 5. 实用考虑事项。 这个课程通过多样的学习材料和实践作业，帮助您掌握混合模型的概念和应用。
Name:Basic concepts on Mixture Models
Description:This module defines mixture models, discusses its properties, and develops the likelihood function for a random sample from a mixture model that will be the basis for statistical learning.
Name:Maximum likelihood estimation for Mixture Models
Description:
Name:Bayesian estimation for Mixture Models
Description:
Name:Applications of Mixture Models
Description:
Name:Practical considerations
Description:
Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application. Some exercises require the use of R, a freely-available statistical software package. A brief tutorial is provided, but we encourage you to take advantage of the many other resources online for learning R if you are interested. This is an intermediate-level course, and it was designed to be the third in UC Santa Cruz's series on Bayesian statistics, after Herbie Lee's "Bayesian Statistics: From Concept to Data Analysis" and Matthew Heiner's "Bayesian Statistics: Techniques and Models." To succeed in the course, you should have some knowledge of and comfort with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
