Bayesian Statistics: Mixture Models
This course is part of Bayesian Statistics Specialization
Instructor: Abel Rodriguez
What you'll learn
Skills you'll gain
There are 5 modules in this course
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.
Maximum likelihood estimation for Mixture Models
Bayesian estimation for Mixture Models
Applications of Mixture Models
Practical considerations
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