MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
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 advanced 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.
Syllabus
WEEK 1
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.
WEEK 2
Maximum likelihood estimation for Mixture Models
WEEK 3
Bayesian estimation for Mixture Models
WEEK 4
Applications of Mixture Models
WEEK 5
Practical considerations
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.