Bayesian Statistics: Mixture Models
Bayesian Statistics: Mixture Models introduces you to a class of statistical models known as Bayesian statistics. Each of the five modules contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Because statistics is best learned by doing rather than watching a video, the course is designed to assist you in learning through application.
R, a free statistical software package, is required for some exercises. A brief tutorial is provided, but if you are interested in learning R, you are recommended to use the many other resources available online.
After Herbie Lee's "Bayesian Statistics: From Concept to Data Analysis" and Matthew Heiner's "Bayesian Statistics: Techniques and Models," this is an intermediate-level course that was designed to be the third in UC Santa Cruz's Bayesian statistics series. You should be familiar with calculus-based probability, maximum-likelihood estimation principles, and Bayesian estimation in order to succeed in this course.
This course offers:
- Flexible deadlines: Reset deadlines based on your availability.
- Shareable certificate: Get a Certificate when you complete
- 100% online
- Course 3 of 5 in the Bayesian Statistics Specialization
- Intermediate level: Familiarity with calculus-based probability, principles of maximum-likelihood estimation, and Bayesian estimation.
- Approx. 22 hours to finish
- Subtitles: French, Portuguese (European), Russian, English, Spanish
Course ratings: 4.6/5
Enroll here: https://www.coursera.org/learn/mixture-models
