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.
提供方
贝叶斯统计 专项课程加州大学圣克鲁兹分校课程信息
9,097 次近期查看
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 3 门课程（共 5 门）
中级
Familiarity with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
完成时间大约为22 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
- Markov Model
- Bayesian Statistics
- Mixture Model
- R Programming
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 3 门课程（共 5 门）
中级
Familiarity with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
完成时间大约为22 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
提供方
加州大学圣克鲁兹分校
UC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience.
授课大纲 - 您将从这门课程中学到什么
完成时间为 4 小时
Basic concepts on Mixture Models
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.
完成时间为 4 小时
9 个视频 （总计 94 分钟）, 7 个阅读材料, 9 个测验
完成时间为 4 小时
Maximum likelihood estimation for Mixture Models
完成时间为 4 小时
4 个视频 （总计 73 分钟）, 2 个阅读材料, 2 个测验
完成时间为 4 小时
Bayesian estimation for Mixture Models
完成时间为 4 小时
6 个视频 （总计 84 分钟）, 2 个阅读材料, 2 个测验
完成时间为 5 小时
Applications of Mixture Models
完成时间为 5 小时
7 个视频 （总计 108 分钟）, 3 个阅读材料, 3 个测验
审阅
- 5 stars66.66%
- 4 stars24.24%
- 3 stars9.09%
来自BAYESIAN STATISTICS: MIXTURE MODELS的热门评论
由 SM 提供Jan 19, 2021
I learned a lot about bayesian mixture model, expectation maximization, and MCMC algorithms and their use case in classification and clustering problems. I highly recommend this course.
由 CM 提供May 17, 2021
Definitely quite mathematical in nature. Good way to learn about expectation-maximisation algorithm.
关于 贝叶斯统计 专项课程
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
常见问题
我什么时候能够访问课程视频和作业？
我订阅此专项课程后会得到什么？
有助学金吗？
还有其他问题吗？请访问 学生帮助中心。
